Parameters for Use in Particle Discrimination

ABSTRACT

Aspects of the present disclosure include methods for characterizing particles of a sample in a flow stream. Methods according to certain embodiments include detecting light from a sample having cells in a flow stream, generating an image of an object in the flow stream in an interrogation region and determining whether the object in the flow stream is an aggregate based on the generated image. Systems having a processor with memory operably coupled to the processor having instructions stored thereon, which when executed by the processor, cause the processor to generate an image of an object in a flow stream and to determine whether the object is an aggregate are also described. Integrated circuit devices (e.g., field programmable gate arrays) having programming for practicing the subject methods are also provided.

CROSS-REFERENCE TO RELATED APPLICATION

This application is related to U.S. Provisional Patent Application Ser.No. 62/826,646 filed Mar. 29, 2019; the disclosure of which applicationis herein incorporated by reference.

INTRODUCTION

Flow-type particle sorting systems, such as sorting flow cytometers, areused to sort particles in a fluid sample based on at least one measuredcharacteristic of the particles. In a flow-type particle sorting system,particles, such as molecules, analyte-bound beads, or individual cvells,in a fluid suspension are passed in a stream by a detection region inwhich a sensor detects particles contained in the stream of the type tobe sorted. The sensor, upon detecting a particle of the type to besorted, triggers a sorting mechanism that selectively isolates theparticle of interest. To sort particles in the sample, a drop-chargingmechanism charges droplets of the flow stream that contain a particletype to be sorted with an electrical charge at the break-off point ofthe flow stream. Droplets are passed through an electrostatic field andare deflected based on polarity and magnitude of charge on the dropletinto one or more collection containers. Uncharged droplets are notdeflected by the electrostatic field.

In particle sorting, aggregates (e.g., clumping of cells) can beprominent components of samples, affecting the accuracy andreproducibility of sorting protocols. In addition, mischaracterizing anaggregate of cells as a single cell reduces overall yield and purity ofsorted cells, which can be harmful in particular when purity is criticalto the ultimate use of a sorted cell composition (e.g., as atherapeutic). Clumping may occur due to incomplete disruption of tissuesby mechanical or enzymatic break down into single cells, by the use ofalcohol-based fixatives that induce clumping or by centrifugation.Clumping may also occur as an inherent attribute of certain cell types,such as keratinocytes.

SUMMARY

Aspects of the present disclosure include methods for characterizingparticles of a sample in a flow stream. Methods according to certainembodiments include detecting light from a sample having cells in a flowstream, generating spatial data of an object in the flow stream in aninterrogation region and determining whether the object in the flowstream is an aggregate based on the spatial data. Systems having aprocessor with memory operably coupled to the processor havinginstructions stored thereon, which when executed by the processor, causethe processor to generate spatial data of an object in a flow stream andto determine whether the object is an aggregate are also described.Integrated circuit devices (e.g., field programmable gate arrays) havingprogramming for practicing the subject methods are also provided.

In embodiments, light from a sample in a flow stream is detected in aninterrogation region and one or more images (e.g., frequency-encodedimages) of objects in the flow stream are generated. In someembodiments, the objects imaged in the interrogation region includecells. In some embodiments, methods include detecting one or more oflight absorption, light scatter, light emission (e.g., fluorescence)from the sample in the flow stream. In some instances, spatial data ofone or more objects in the sample is generated from detected lightabsorption (e.g., brightfield image data). In other instances, spatialdata of one or more objects in the sample is generated from detectedlight scatter (e.g., forward scatter image data, side scatter imagedata). In yet other instances, spatial data of one or more objects inthe sample are generated from detected fluorescence (e.g., fluorescentmarker image data). In still other instances, spatial data of one ormore objects in the sample is generated from a combination of two ormore of detected light absorption, detected light scatter and detectedfluorescence.

In some embodiments, methods include determining the size of the objectbased on the spatial data. In other embodiments, methods includedetermining the center of mass of the object based on the spatial data.In yet other embodiments, methods include determining the eccentricityof the object based on the spatial data. In certain embodiments, animage moment is calculated based on the spatial data. In some instances,methods include calculating a first order image moment of the objectalong a horizontal axis. In other instances, methods include calculatinga second order image moment of the object along a horizontal axis. Inyet other instances, methods include calculating a first order imagemoment of the object along a vertical axis. In still other instances,methods include calculating a second order image moment of the objectalong a vertical axis.

In some embodiments, methods include generating an image of an object inthe flow in an interrogation region. In some embodiments, the image is agreyscale image of the object. Methods according to certain embodimentsinclude calculating an image moment of the object from the generatedimage. In some instances, methods include calculating a first orderimage moment of the object along a horizontal axis. In other instances,methods include calculating a second order image moment of the objectalong a horizontal axis. In yet other instances, methods includecalculating a first order image moment of the object along a verticalaxis. In still other instances, methods include calculating a secondorder image moment of the object along a vertical axis.

In some embodiments, one or more properties of the object is determinedbased on the calculated image moment and generated image. For example,methods may include determining the size of the object, the center ofmass, the eccentricity of the object along a horizontal axis or verticalaxis or a combination thereof. In some instances, methods includeassessing one or more of the size, center of mass and eccentricity ofthe object and determining whether the object is a cell aggregate. Incertain instances, the object is determined to be a cell aggregate basedon the determined size and center of mass of the object. In otherinstances, the object is determined to be a cell aggregate based on thedetermined size and eccentricity of the object. In yet other instances,the object is determined to be a cell aggregate based on the determinedcenter of mass and eccentricity of the object. In still other instances,the object is determined to be a cell aggregate based on the determinedsize, center of mass and eccentricity of the object. In certaininstances, methods include comparing a first image of the object with asecond image of the object and determining one or more properties of theobject based on the comparison between the first image of the objectwith the second image of the object.

In determining whether the object is a cell aggregate, in certaininstances, methods include: 1) assessing one or more properties of theobject based on the calculated image moment and spatial data; and 2)assessing light scatter detector output signals from the object in theinterrogation region of the flow stream. In some embodiments, the lightscatter includes forward scattered light from the object. In otherembodiments, the light scatter includes side scattered light from theobject. In certain embodiments, methods include assessing the lightscatter detector output signals for one or more of the pulse width, thepulse height and pulse area.

In some embodiments, methods include: 1) assessing one or moreproperties of the object based on the calculated image moment andgenerated image; and 2) assessing light scatter detector output signalsfrom the object in the interrogation region of the flow stream. In someembodiments, the light scatter includes forward scattered light from theobject. In other embodiments, the light scatter includes side scatteredlight from the object. In certain embodiments, methods include assessingthe light scatter detector output signals for one or more of the pulsewidth, the pulse height and pulse area.

In certain embodiments, methods include generating an image mask of theobject. To generate an image mask according to some instances, methodsinclude generating a greyscale image of the object in the flow stream,determining a pixel intensity threshold value from the greyscale image,comparing each pixel from the greyscale image against the determinedpixel intensity threshold value and converting each pixel to a binarypixel value. In one example, methods include detecting light absorption(e.g., brightfield image data) from the flow stream and assigning apixel value of 1 to each pixel in the greyscale image when the pixelintensity is less than the threshold value and assigning a pixel valueof 0 when the pixel intensity of the greyscale image is greater than thethreshold value. In another example, methods include detecting lightscatter from the cell in the flow stream and assigning a pixel value of1 to each pixel in the greyscale image when the pixel intensity isgreater than the threshold value and assigning a pixel value of 0 whenthe pixel intensity is less than the threshold value. In yet anotherexample, methods include detecting fluorescence from the cell in theflow stream and assigning a pixel value of 1 to each pixel in thegreyscale image when the pixel intensity is greater than the thresholdvalue and assigning a pixel value of 0 when the pixel intensity is lessthan the threshold value. In some embodiments, the image mask isgenerated from the pixels having a binary pixel value of 1. In otherembodiments, the image mask is generated from the pixels having a binarypixel value of 0.

In some embodiments, methods include determining the size of the object,the center of mass or the eccentricity of the object along a horizontalaxis or vertical axis based on the generated image mask. In theseembodiments, one or more of these parameters from the image mask areused to assess whether the object is a single cell or is a cellaggregate. In one example, methods include assessing the size and thecenter of mass of the object from the image mask to determine whetherthe object is a single cell or a cell aggregate. In another example,methods include assessing the eccentricity along a horizontal axis orvertical axis and the size of the object from the image mask todetermine whether the object is a single cell or a cell aggregate. Inyet another example, methods include assessing the eccentricity along ahorizontal axis or vertical axis and the center of mass from the imagemask to determine whether the object is a single cell or a cellaggregate.

In some embodiments, methods include identifying that the object is acell aggregate. In some instances, methods include determining that theobject is a horizontal cell aggregate where two or more cells arealigned together across a horizontal axis of the flow stream. In otherinstances, methods include determining that the object is a verticalcell aggregate where two or more cells are aligned together along avertical axis (i.e., longitudinal axis) of the flow stream. In yet otherinstances, methods include determining that the object is combinationcell aggregate having two or more cells aligned together across ahorizontal axis and having two or more cells aligned together along avertical axis.

In some embodiments, methods include calculating the spatial data fromfrequency-encoded fluorescence data from the object. In some instances,calculating the spatial data of the object includes performing atransform of the frequency-encoded fluorescence data. In one example,the spatial data is calculated by performing a Fourier transform (FT) ofthe frequency-encoded fluorescence data. In another example, the spatialdata is calculated by performing a discrete Fourier transform (DFT) ofthe frequency-encoded fluorescence data. In yet another example, thespatial data is calculated by performing a short time Fourier transform(STFT) of the frequency-encoded fluorescence data. In still anotherexample, the spatial data is calculated with a digital lock-in amplifierto heterodyne and de-multiplex the frequency-encoded fluorescence data.

Methods according to certain embodiments also include sorting theobject. In some embodiments, the object is identified as being a singlecell and is sorted to a first sample component collection location. Inother embodiments, the object is identified as being a cell aggregateand is sorted to a second sample component collection location. In someinstances, the first sample component collection location includes asample collection container and the second sample component collectionlocation includes a waste collection container.

Aspects of the present disclosure also include systems forcharacterizing particles of a sample (e.g., cells in a biologicalsample). Systems according to certain embodiments include a light sourceconfigured to irradiate a sample having cells in a flow stream, a lightdetection system having a photodetector and a processor having memoryoperably coupled to the processor such that the memory includesinstructions stored thereon, which when executed by the processor, causethe processor to generate spatial data of an object in the flow streamin an interrogation region and to determine whether the object in theflow stream is a cell aggregate based on the spatial data. Inembodiments, the light detection system includes one or morephotodetectors for detecting light absorption, light scatter,fluorescence or a combination thereof.

In some embodiments, systems include a processor with memory operablycoupled to the processor such that the memory includes instructionsstored thereon, which when executed by the processor, cause theprocessor to determine the size of the object based on the spatial data.In other embodiments, the memory includes instructions for determiningthe center of mass of the object based on the spatial data. In yet otherembodiments, the memory includes instructions for determining theeccentricity of the object based on the spatial data. In certainembodiments, the memory includes instructions for calculating an imagemoment based on the spatial data. In some instances, the memory includesinstructions for calculating a first order image moment of the objectalong a horizontal axis. In other instances, the memory includesinstructions for calculating a second order image moment of the objectalong a horizontal axis. In yet other instances, the memory includesinstructions for calculating a first order image moment of the objectalong a vertical axis. In still other instances, the memory includesinstructions for calculating a second order image moment of the objectalong a vertical axis.

In some embodiments, systems include a processor with memory operablycoupled to the processor such that the memory includes instructionsstored thereon, which when executed by the processor, cause theprocessor to generate an image of an object in the flow stream. In someembodiments, the memory includes instructions for generating a greyscaleimage of the object in the flow stream. In some embodiments, systemsinclude a computer program that includes instructions for generating theimage from detected light absorption (e.g., brightfield image data) fromthe object in the flow stream. In other embodiments, systems include acomputer program that includes instructions for generating the imagefrom detected light scatter (e.g., forward scatter image data, sidescatter image data) from the object in the flow stream. In yet otherembodiments, systems include a computer program that includesinstructions for generating the image from detected fluorescence (e.g.,fluorescent marker image data) from the object in the flow stream. Instill other instances, systems include a computer program that includesinstructions for generating an image of the object from a combination oftwo or more of detected light absorption, detected light scatter anddetected fluorescence.

In some embodiments, the memory includes instructions which whenexecuted by the processor cause the processor to calculate an imagemoment of the object from the generated image. In some instances,systems include a computer program that includes instructions forcalculating a first order image moment of the object along a horizontalaxis. In other instances, systems include a computer program thatincludes instructions for calculating a second order image moment of theobject along a horizontal axis. In yet other instances, systems includea computer program that includes instructions for calculating a firstorder image moment of the object along a vertical axis. In still otherinstances, systems include a computer program that includes instructionsfor calculating a second order image moment of the object along avertical axis.

Systems of interest may also include memory having instructions whichwhen executed by the processor, cause the processor to determine one ormore properties of the object in the flow stream based on the calculatedimage moment and generated image. In these embodiments, the memory mayinclude instructions for determining the size of the object, the centerof mass of the object or the eccentricity of the object along ahorizontal axis or a vertical axis or a combination thereof. In someembodiments, systems include a computer program that includesinstructions for assessing one or more of the size, center of mass andeccentricity of the object and determining whether the object is a cellaggregate. In one example, systems include a computer program thatincludes instructions for determining that the object is a cellaggregate based on the determined size and center of mass of the object.In another example, systems include a computer program that includesinstructions for determining that the object is a cell aggregate basedon the determined size and eccentricity of the object. In yet anotherexample, systems include a computer program that includes instructionsfor determining that the object is a cell aggregate based on thedetermined center of mass and eccentricity of the object. In stillanother example, systems include a computer program that includesinstructions for determining that the object is a cell aggregate basedon the determined size, center of mass and eccentricity of the object.In certain instances, systems include a computer program that includesinstructions for comparing a first image of the object with a secondimage of the object and determining one or more properties of the objectbased on the comparison between the first image of the object with thesecond image of the object.

In certain instances, systems include a computer program that includesinstructions for: 1) assessing one or more properties of the objectbased on the calculated image moment and spatial data; and 2) assessinglight scatter detector output signals from the object in theinterrogation region of the flow stream. In some embodiments, systemsare configured to assess output signals from a forward scatter lightdetector. In other embodiments, systems are configured to assess outputsignals from a side scatter light detector. In certain embodiments,systems include a computer program that includes instructions forassessing the light scatter detector output signals for one or more ofthe pulse width, the pulse height and pulse area.

In certain instances, systems include a computer program that includesinstructions for: 1) assessing one or more properties of the objectbased on the calculated image moment and generated image; and 2)assessing light scatter detector output signals from the object in theinterrogation region of the flow stream. In some embodiments, systemsare configured to assess output signals from a forward scatter lightdetector. In other embodiments, systems are configured to assess outputsignals from a side scatter light detector. In certain embodiments,systems include a computer program that includes instructions forassessing the light scatter detector output signals for one or more ofthe pulse width, the pulse height and pulse area.

In certain embodiments, systems of interest also include memory havinginstructions which when executed by the processor, cause the processorto generate an image mask of the object. In these embodiments, thesystem includes a computer program having instructions for: 1)generating a greyscale image of the object in the flow stream; 2)determining a pixel intensity threshold value from the greyscale image;3) comparing each pixel from the greyscale image against the determinedpixel intensity threshold value and 4) converting each pixel to a binarypixel value. In one example, the system includes a computer programhaving instructions for detecting light absorption (e.g., brightfieldimage data) from the flow stream and assigning a pixel value of 1 toeach pixel in the greyscale image when the pixel intensity is less thanthe threshold value and assigning a pixel value of 0 when the pixelintensity of the greyscale image is greater than the threshold value. Inanother example, the system includes a computer program havinginstructions for detecting light scatter from the cell in the flowstream and assigning a pixel value of 1 to each pixel in the greyscaleimage when the pixel intensity is greater than the threshold value andassigning a pixel value of 0 when the pixel intensity is less than thethreshold value. In yet another example, the system includes a computerprogram having instructions for detecting fluorescence from the cell inthe flow stream and assigning a pixel value of 1 to each pixel in thegreyscale image when the pixel intensity is greater than the thresholdvalue and assigning a pixel value of 0 when the pixel intensity is lessthan the threshold value. In some embodiments, the system is configuredto generate the image mask from the pixels having a binary pixel valueof 1. In other embodiments, the system is configured to generate theimage mask from the pixels having a binary pixel value of 0.

The subject systems are configured, according to certain instances, todiscriminate between objects in the sample. In some instances, thesystem includes a computer program having instructions for determiningthe size of the object, the center of mass or the eccentricity of theobject along a horizontal axis or vertical axis based on the generatedimage mask. In these instances, the subject system uses one or more ofthese parameters from the image mask to assess whether the object is asingle cell or is a cell aggregate. In one example, the system includesa computer program having instructions for assessing the size and thecenter of mass of the object from the image mask and determining whetherthe object is a single cell or a cell aggregate. In another example, thesystem includes a computer program having instructions for assessing theeccentricity along a horizontal axis or vertical axis and the size ofthe object from the image mask and determining whether the object is asingle cell or a cell aggregate. In yet another example, the systemincludes a computer program having instructions for assessing theeccentricity along a horizontal axis or vertical axis and the center ofmass of the object from the image mask and determining whether theobject is a single cell or a cell aggregate.

In some embodiments, systems of interest include memory havinginstructions which when executed by the processor, cause the processorto identify that the object is a cell aggregate. In some instances,systems are configured to classify the object as being a horizontal cellaggregate where two or more cells are aligned together across ahorizontal axis of the flow stream. In other instances, systems areconfigured to classify the object as being a vertical cell aggregatewhere two or more cells are aligned together along a vertical axis(i.e., longitudinal axis) of the flow stream. In yet other instances,systems are configured to classify the object as being a combinationcell aggregate having two or more cells aligned together across ahorizontal axis and having two or more cells aligned together along avertical axis.

In some embodiments, systems of interest include memory havinginstructions which when executed by the processor, cause the processorto calculate the spatial data from frequency-encoded fluorescence datafrom the object. In some instances, calculating the spatial data of theobject includes performing a transform of the frequency-encodedfluorescence data. In one example, the spatial data is calculated byperforming a Fourier transform (FT) of the frequency-encodedfluorescence data. In another example, the spatial data is calculated byperforming a discrete Fourier transform (DFT) of the frequency-encodedfluorescence data. In yet another example, the spatial data iscalculated by performing a short time Fourier transform (SIFT) of thefrequency-encoded fluorescence data. In still another example, thespatial data is calculated with a digital lock-in amplifier toheterodyne and de-multiplex the frequency-encoded fluorescence data.

Systems of interest are configured for sorting particles of a sample(e.g., a biological sample) in the flow stream. In some embodiments,systems further include a particle sorting component having a samplefluid delivery subsystem and a sheath fluid delivery subsystem that isin fluid communication with an inlet of the particle sorting componentand one or more sample collection containers for receiving the sortedobject from the flow stream. In certain instances, the object isdetermined to be a cell aggregate and the sorting component isconfigured to direct the cell aggregate to a waste collection outlet(e.g., waste conduit or container). In other instances, the object isdetermined to be a single cell and the sorting component is configuredto direct the single cell to a sample collection container.

Aspects of the present disclosure also include integrated circuitdevices programmed to: generate spatial data of an object in a flowstream in an interrogation region; and determine whether the object inthe flow stream is a cell aggregated based on the spatial data. In someembodiments, integrated circuit devices are programmed to sort theobject, such as to a waste collection container when the object isdetermined to be a cell aggregate or to a sample collection containerwhen the object is determined to be a single cell. Integrated circuitdevices of interest may include, in certain instances, a fieldprogrammable gate array (FPGA), an application specific integratedcircuit (ASIC) or a complex programmable logic device (CPLD).

Integrated circuit devices according to certain embodiments areprogrammed to generate spatial data of an object in the flow stream. Insome embodiments, the integrated circuit device is programmed togenerate spatial data from data signals from a light absorption detector(e.g., brightfield image data). In other embodiments, the integratedcircuit device is programmed to generate spatial data from data signalsfrom a light scatter detector (e.g., forward scatter image data, sidescatter image data). In yet other embodiments, the integrated circuitdevice is programmed to generate spatial data from data signals from alight emission detector (e.g., fluorescent marker image data). In stillother instances, the integrated circuit device is programmed to generatespatial data of the object from a combination of two or more of detectedlight absorption, detected light scatter and detected fluorescence.

In some embodiments, the integrated circuit device is programmed fordetermining the size of the object based on the spatial data. In otherembodiments, integrated circuit device is programmed for determining thecenter of mass of the object based on the spatial data. In yet otherembodiments, integrated circuit device is programmed for determining theeccentricity of the object based on the spatial data. In certainembodiments, an image moment is calculated based on the spatial data. Insome instances, integrated circuit device is programmed for calculatinga first order image moment of the object along a horizontal axis. Inother instances, integrated circuit device is programmed for calculatinga second order image moment of the object along a horizontal axis. Inyet other instances, integrated circuit device is programmed forcalculating a first order image moment of the object along a verticalaxis. In still other instances, integrated circuit device is programmedfor calculating a second order image moment of the object along avertical axis.

In some embodiments, the integrated circuit device is programmed tocalculate an image moment of the object from the generated image. Insome instances, the integrated circuit device is programmed to calculatea first order image moment of the object along a horizontal axis. Inother instances, the integrated circuit device is programmed tocalculate a second order image moment of the object along a horizontalaxis. In yet other instances, the integrated circuit device isprogrammed to calculate a first order image moment of the object along avertical axis. In still other instances, the integrated circuit deviceis programmed to calculate a second order image moment of the objectalong a vertical axis.

In some embodiments, the integrated circuit device is programmed todetermine one or more properties of the object in the flow stream basedon the calculated image moment and generated image. In theseembodiments, the integrated circuit device is programmed to determinethe size of the object, the center of mass of the object or theeccentricity of the object along a horizontal axis or a vertical axis ora combination thereof. In some embodiments the integrated circuit deviceis programmed to assess one or more of the size, center of mass andeccentricity of the object and determine whether the object is a cellaggregate. In one example, the integrated circuit device is programmedto determine that the object is a cell aggregate based on the determinedsize and center of mass of the object. In another example, theintegrated circuit device is programmed to determine that the object isa cell aggregate based on the determined size and eccentricity of theobject. In yet another example, systems include the integrated circuitdevice is programmed to determine that the object is a cell aggregatebased on the determined center of mass and eccentricity of the object.In still another example, the integrated circuit device is programmed todetermine that the object is a cell aggregate based on the determinedsize, center of mass and eccentricity of the object. In certaininstances, the integrated circuit device is programmed to compare afirst image of the object with a second image of the object anddetermine one or more properties of the object based on the comparisonbetween the first image of the object with the second image of theobject.

In certain instances, the integrated circuit device is programmed to: 1)assess one or more properties of the object based on the calculatedimage moment and spatial data; and 2) assess light scatter detectoroutput signals from the object in the interrogation region of the flowstream. In some embodiments, the integrated circuit device is programmedto assess output signals from a forward scatter light detector. In otherembodiments, the integrated circuit device is programmed to assessoutput signals from a side scatter light detector. In certainembodiments, the integrated circuit device is programmed to assess thelight scatter detector output signals for one or more of the pulsewidth, the pulse height and pulse area.

In certain instances, the integrated circuit device is programmed to: 1)assess one or more properties of the object based on the calculatedimage moment and generated image; and 2) assess light scatter detectoroutput signals from the object in the interrogation region of the flowstream. In some embodiments, the integrated circuit device is programmedto assess output signals from a forward scatter light detector. In otherembodiments, the integrated circuit device is programmed to assessoutput signals from a side scatter light detector. In certainembodiments, the integrated circuit device is programmed to assess thelight scatter detector output signals for one or more of the pulsewidth, the pulse height and pulse area.

In certain embodiments, the integrated circuit device is programmed togenerate an image mask of the object. In these embodiments, theintegrated circuit device is programmed to: 1) generate a greyscaleimage of the object in the flow stream; 2) determine a pixel intensitythreshold value from the greyscale image; 3) compare each pixel from thegreyscale image against the determined pixel intensity threshold valueand 4) convert each pixel to a binary pixel value. In one example, theintegrated circuit device is programmed to receive data signals from alight absorption detector (e.g., brightfield image data) and assign apixel value of 1 to each pixel in the greyscale image when the pixelintensity is less than the threshold value and assigning a pixel valueof 0 when the pixel intensity of the greyscale image is greater than thethreshold value. In another example, the integrated circuit device isprogrammed to receive data signals from a light scatter detector andassign a pixel value of 1 to each pixel in the greyscale image when thepixel intensity is greater than the threshold value and assigning apixel value of 0 when the pixel intensity is less than the thresholdvalue. In yet another example, the integrated circuit device isprogrammed to receive data signals from a fluorescence detector andassign a pixel value of 1 to each pixel in the greyscale image when thepixel intensity is greater than the threshold value and assigning apixel value of 0 when the pixel intensity is less than the thresholdvalue. In some embodiments, the integrated circuit device is programmedto generate the image mask from the pixels having a binary pixel valueof 1. In other embodiments, the integrated circuit device is programmedto generate the image mask from the pixels having a binary pixel valueof 0.

In some instances, the integrated circuit device is programmed todetermine the size of the object, the center of mass or the eccentricityof the object along a horizontal axis or vertical axis based on thegenerated image mask. In these instances, the integrated circuit deviceuses data signals corresponding to one or more of these parameters fromthe image mask to assess whether the object is a single cell or is acell aggregate. In one example, the integrated circuit device isprogrammed to assess the size and the center of mass of the object fromthe image mask and determine whether the object is a single cell or acell aggregate. In another example, the integrated circuit device isprogrammed to assess the eccentricity along a horizontal axis orvertical axis and the size of the object from the image mask anddetermine whether the object is a single cell or a cell aggregate. Inyet another example, the integrated circuit device is programmed toassess the eccentricity along a horizontal axis or vertical axis and thecenter of mass of the object from the image mask and determine whetherthe object is a single cell or a cell aggregate.

In some embodiments, the integrated circuit device is programmed toidentify that the object is a cell aggregate. In some instances, theintegrated circuit device is programmed to classify the object as beinga horizontal cell aggregate where two or more cells are aligned togetheracross a horizontal axis of the flow stream. In other instances, theintegrated circuit device is programmed to classify the object as beinga vertical cell aggregate where two or more cells are aligned togetheralong a vertical axis (i.e., longitudinal axis) of the flow stream. Inyet other instances, the integrated circuit device is programmed toclassify the object as being a combination cell aggregate having two ormore cells aligned together across a horizontal axis and having two ormore cells aligned together along a vertical axis.

BRIEF DESCRIPTION OF THE FIGURES

The invention may be best understood from the following detaileddescription when read in conjunction with the accompanying drawings.Included in the drawings are the following figures:

FIGS. 1, 1A and 1B depict images of cell aggregates according to certainembodiments. FIG. 1A depicts an image of a vertically oriented cellaggregate having two cells aligned together along a vertical axis. FIG.1B depicts an image of a horizontally oriented cell aggregate having twocells aligned together along a horizontal axis.

FIGS. 2, 2A and 2B depict the use of calculated eccentricity todiscriminate between a single cell and an aggregate of cells accordingto certain embodiments. FIG. 2A depicts an imaged single cell. FIG. 2Bdepicts an imaged cell aggregate.

FIG. 3 depicts a flow chart for imaging and characterizing a particle ina flow stream according to certain embodiments.

DETAILED DESCRIPTION

Aspects of the present disclosure include methods for characterizingparticles of a sample in a flow stream. Methods according to certainembodiments include detecting light from a sample having cells in a flowstream, generating an image of an object in the flow stream in aninterrogation region and determining whether the object in the flowstream is an aggregate based on the generated image. Systems having aprocessor with memory operably coupled to the processor havinginstructions stored thereon, which when executed by the processor, causethe processor to generate an image of an object in a flow stream and todetermine whether the object is an aggregate are also described.Integrated circuit devices (e.g., field programmable gate arrays) havingprogramming for practicing the subject methods are also provided.

Before the present invention is described in greater detail, it is to beunderstood that this invention is not limited to particular embodimentsdescribed, as such may, of course, vary. It is also to be understoodthat the terminology used herein is for the purpose of describingparticular embodiments only, and is not intended to be limiting, sincethe scope of the present invention will be limited only by the appendedclaims.

Where a range of values is provided, it is understood that eachintervening value, to the tenth of the unit of the lower limit unlessthe context clearly dictates otherwise, between the upper and lowerlimit of that range and any other stated or intervening value in thatstated range, is encompassed within the invention. The upper and lowerlimits of these smaller ranges may independently be included in thesmaller ranges and are also encompassed within the invention, subject toany specifically excluded limit in the stated range. Where the statedrange includes one or both of the limits, ranges excluding either orboth of those included limits are also included in the invention.

Certain ranges are presented herein with numerical values being precededby the term “about.” The term “about” is used herein to provide literalsupport for the exact number that it precedes, as well as a number thatis near to or approximately the number that the term precedes. Indetermining whether a number is near to or approximately a specificallyrecited number, the near or approximating unrecited number may be anumber which, in the context in which it is presented, provides thesubstantial equivalent of the specifically recited number.

Unless defined otherwise, all technical and scientific terms used hereinhave the same meaning as commonly understood by one of ordinary skill inthe art to which this invention belongs. Although any methods andmaterials similar or equivalent to those described herein can also beused in the practice or testing of the present invention, representativeillustrative methods and materials are now described.

All publications and patents cited in this specification are hereinincorporated by reference as if each individual publication or patentwere specifically and individually indicated to be incorporated byreference and are incorporated herein by reference to disclose anddescribe the methods and/or materials in connection with which thepublications are cited. The citation of any publication is for itsdisclosure prior to the filing date and should not be construed as anadmission that the present invention is not entitled to antedate suchpublication by virtue of prior invention. Further, the dates ofpublication provided may be different from the actual publication dateswhich may need to be independently confirmed.

It is noted that, as used herein and in the appended claims, thesingular forms “a”, “an”, and “the” include plural referents unless thecontext clearly dictates otherwise. It is further noted that the claimsmay be drafted to exclude any optional element. As such, this statementis intended to serve as antecedent basis for use of such exclusiveterminology as “solely,” “only” and the like in connection with therecitation of claim elements, or use of a “negative” limitation.

As will be apparent to those of skill in the art upon reading thisdisclosure, each of the individual embodiments described and illustratedherein has discrete components and features which may be readilyseparated from or combined with the features of any of the other severalembodiments without departing from the scope or spirit of the presentinvention. Any recited method can be carried out in the order of eventsrecited or in any other order which is logically possible.

While the apparatus and method has or will be described for the sake ofgrammatical fluidity with functional explanations, it is to be expresslyunderstood that the claims, unless expressly formulated under 35 U.S.C.§ 112, are not to be construed as necessarily limited in any way by theconstruction of “means” or “steps” limitations, but are to be accordedthe full scope of the meaning and equivalents of the definition providedby the claims under the judicial doctrine of equivalents, and in thecase where the claims are expressly formulated under 35 U.S.C. § 112 areto be accorded full statutory equivalents under 35 U.S.C. § 112.

As summarized above, the present disclosure provides systems and methodsfor determining whether an object in a flow stream is a cell aggregate.

In further describing embodiments of the disclosure, methods forgenerating spatial data of the object in a flow stream and determiningwhether the object is a cell aggregate based on the spatial data arefirst described in greater detail. Next, systems for characterizingobjects in a flow stream and separating particles in a sample in realtime are described. Integrated circuit devices, such as fieldprogrammable gate arrays having programming for generating spatial dataof the object in the flow stream, classifying the object as being a cellaggregate or a single cell and sorting the cell aggregate or single cellare also provided.

Methods for Characterizing Particles in a Sample

Aspects of the present disclosure include methods for characterizingparticles of a sample (e.g., cells in a biological sample). Inpracticing methods according to certain embodiments, a sample havingcells in a flow stream is irradiated with a light source and light fromthe sample is detected to generate spatial data of an object in the flowstream in an interrogation region and to determine whether the object isan aggregate of particles (e.g., aggregate of cells) based on thespatial data. In embodiments, methods include discriminating betweensingle cells and an aggregate of two or more cells based on the spatialdata of the object. The term “spatial data” is used herein to describedata signals encoding spatial positions of the irradiated interrogationregion of the flow stream. As described in greater detail below, in someembodiments the spatial data is calculated from frequency-encodedfluorescence data from the object in the flow stream, such as a byperforming a transform of frequency-encoded fluorescence data (e.g.,performing a Fourier transform or calculating the spatial data with adigital lock-in amplifier to heterodyne and de-multiplex thefrequency-encoded fluorescence data) In some embodiments, the sample isa biological sample. The term “biological sample” is used in itsconventional sense to refer to a whole organism, plant, fungi or asubset of animal tissues, cells or component parts which may in certaininstances be found in blood, mucus, lymphatic fluid, synovial fluid,cerebrospinal fluid, saliva, bronchoalveolar lavage, amniotic fluid,amniotic cord blood, urine, vaginal fluid and semen. As such, a“biological sample” refers to both the native organism or a subset ofits tissues as well as to a homogenate, lysate or extract prepared fromthe organism or a subset of its tissues, including but not limited to,for example, plasma, serum, spinal fluid, lymph fluid, sections of theskin, respiratory, gastrointestinal, cardiovascular, and genitourinarytracts, tears, saliva, milk, blood cells, tumors, organs. Biologicalsamples may be any type of organismic tissue, including both healthy anddiseased tissue (e.g., cancerous, malignant, necrotic, etc.). In certainembodiments, the biological sample is a liquid sample, such as blood orderivative thereof, e.g., plasma, tears, urine, semen, etc., where insome instances the sample is a blood sample, including whole blood, suchas blood obtained from venipuncture or fingerstick (where the blood mayor may not be combined with any reagents prior to assay, such aspreservatives, anticoagulants, etc.).

In certain embodiments the source of the sample is a “mammal” or“mammalian”, where these terms are used broadly to describe organismswhich are within the class mammalia, including the orders carnivore(e.g., dogs and cats), rodentia (e.g., mice, guinea pigs, and rats), andprimates (e.g., humans, chimpanzees, and monkeys). In some instances,the subjects are humans. The methods may be applied to samples obtainedfrom human subjects of both genders and at any stage of development(i.e., neonates, infant, juvenile, adolescent, adult), where in certainembodiments the human subject is a juvenile, adolescent or adult. Whilethe present invention may be applied to samples from a human subject, itis to be understood that the methods may also be carried-out on samplesfrom other animal subjects (that is, in “non-human subjects”) such as,but not limited to, birds, mice, rats, dogs, cats, livestock and horses.

In practicing the subject methods, a sample having cells (e.g., in aflow stream of a flow cytometer) is irradiated with light from a lightsource. In some embodiments, the light source is a broadband lightsource, emitting light having a broad range of wavelengths, such as forexample, spanning 50 nm or more, such as 100 nm or more, such as 150 nmor more, such as 200 nm or more, such as 250 nm or more, such as 300 nmor more, such as 350 nm or more, such as 400 nm or more and includingspanning 500 nm or more. For example, one suitable broadband lightsource emits light having wavelengths from 200 nm to 1500 nm. Anotherexample of a suitable broadband light source includes a light sourcethat emits light having wavelengths from 400 nm to 1000 nm. Wheremethods include irradiating with a broadband light source, broadbandlight source protocols of interest may include, but are not limited to,a halogen lamp, deuterium arc lamp, xenon arc lamp, stabilizedfiber-coupled broadband light source, a broadband LED with continuousspectrum, super-luminescent emitting diode, semiconductor light emittingdiode, wide spectrum LED white light source, an multi-LED integratedwhite light source, among other broadband light sources or anycombination thereof.

In other embodiments, methods includes irradiating with a narrow bandlight source emitting a particular wavelength or a narrow range ofwavelengths, such as for example with a light source which emits lightin a narrow range of wavelengths like a range of 50 nm or less, such as40 nm or less, such as 30 nm or less, such as 25 nm or less, such as 20nm or less, such as 15 nm or less, such as 10 nm or less, such as 5 nmor less, such as 2 nm or less and including light sources which emit aspecific wavelength of light (i.e., monochromatic light). Where methodsinclude irradiating with a narrow band light source, narrow band lightsource protocols of interest may include, but are not limited to, anarrow wavelength LED, laser diode or a broadband light source coupledto one or more optical bandpass filters, diffraction gratings,monochromators or any combination thereof.

In certain embodiments, methods include irradiating the flow stream withone or more lasers. As discussed above, the type and number of laserswill vary depending on the sample as well as desired light collected andmay be a pulsed laser or continuous wave laser. For example, the lasermay be a gas laser, such as a helium-neon laser, argon laser, kryptonlaser, xenon laser, nitrogen laser, CO₂ laser, CO laser, argon-fluorine(ArF) excimer laser, krypton-fluorine (KrF) excimer laser, xenonchlorine (XeCl) excimer laser or xenon-fluorine (XeF) excimer laser or acombination thereof; a dye laser, such as a stilbene, coumarin orrhodamine laser; a metal-vapor laser, such as a helium-cadmium (HeCd)laser, helium-mercury (HeHg) laser, helium-selenium (HeSe) laser,helium-silver (HeAg) laser, strontium laser, neon-copper (NeCu) laser,copper laser or gold laser and combinations thereof; a solid-statelaser, such as a ruby laser, an Nd:YAG laser, NdCrYAG laser, Er:YAGlaser, Nd:YLF laser, Nd:YVO₄ laser, Nd:YCa₄O(BO₃)₃ laser, Nd:YCOB laser,titanium sapphire laser, thulim YAG laser, ytterbium YAG laser,ytterbium₂O₃ laser or cerium doped lasers and combinations thereof; asemiconductor diode laser, optically pumped semiconductor laser (OPSL),or a frequency doubled- or frequency tripled implementation of any ofthe above mentioned lasers.

The sample in the flow stream may be irradiated with one or more of theabove-mentioned light sources, such as 2 or more light sources, such as3 or more light sources, such as 4 or more light sources, such as 5 ormore light sources and including 10 or more light sources. The lightsource may include any combination of types of light sources. Forexample, in some embodiments, the methods include irradiating the samplein the flow stream with an array of lasers, such as an array having oneor more gas lasers, one or more dye lasers and one or more solid-statelasers.

The sample may be irradiated with wavelengths ranging from 200 nm to1500 nm, such as from 250 nm to 1250 nm, such as from 300 nm to 1000 nm,such as from 350 nm to 900 nm and including from 400 nm to 800 nm. Forexample, where the light source is a broadband light source, the samplemay be irradiated with wavelengths from 200 nm to 900 nm. In otherinstances, where the light source includes a plurality of narrow bandlight sources, the sample may be irradiated with specific wavelengths inthe range from 200 nm to 900 nm. For example, the light source may beplurality of narrow band LEDs (1 nm-25 nm) each independently emittinglight having a range of wavelengths between 200 nm to 900 nm. In otherembodiments, the narrow band light source includes one or more lasers(such as a laser array) and the sample is irradiated with specificwavelengths ranging from 200 nm to 700 nm, such as with a laser arrayhaving gas lasers, excimer lasers, dye lasers, metal vapor lasers andsolid-state laser as described above.

Where more than one light source is employed, the sample may beirradiated with the light sources simultaneously or sequentially, or acombination thereof. For example, the sample may be simultaneouslyirradiated with each of the light sources. In other embodiments, theflow stream is sequentially irradiated with each of the light sources.Where more than one light source is employed to irradiate the samplesequentially, the time each light source irradiates the sample mayindependently be 0.001 microseconds or more, such as 0.01 microsecondsor more, such as 0.1 microseconds or more, such as 1 microsecond ormore, such as 5 microseconds or more, such as 10 microseconds or more,such as 30 microseconds or more and including 60 microseconds or more.For example, methods may include irradiating the sample with the lightsource (e.g. laser) for a duration which ranges from 0.001 microsecondsto 100 microseconds, such as from 0.01 microseconds to 75 microseconds,such as from 0.1 microseconds to 50 microseconds, such as from 1microsecond to 25 microseconds and including from 5 microseconds to 10microseconds. In embodiments where sample is sequentially irradiatedwith two or more light sources, the duration sample is irradiated byeach light source may be the same or different.

The time period between irradiation by each light source may also vary,as desired, being separated independently by a delay of 0.001microseconds or more, such as 0.01 microseconds or more, such as 0.1microseconds or more, such as 1 microsecond or more, such as 5microseconds or more, such as by 10 microseconds or more, such as by 15microseconds or more, such as by 30 microseconds or more and includingby 60 microseconds or more. For example, the time period betweenirradiation by each light source may range from 0.001 microseconds to 60microseconds, such as from 0.01 microseconds to 50 microseconds, such asfrom 0.1 microseconds to 35 microseconds, such as from 1 microsecond to25 microseconds and including from 5 microseconds to 10 microseconds. Incertain embodiments, the time period between irradiation by each lightsource is 10 microseconds. In embodiments where sample is sequentiallyirradiated by more than two (i.e., 3 or more) light sources, the delaybetween irradiation by each light source may be the same or different.

The sample may be irradiated continuously or in discrete intervals. Insome instances, methods include irradiating the sample in the samplewith the light source continuously. In other instances, the sample in isirradiated with the light source in discrete intervals, such asirradiating every 0.001 millisecond, every 0.01 millisecond, every 0.1millisecond, every 1 millisecond, every 10 milliseconds, every 100milliseconds and including every 1000 milliseconds, or some otherinterval.

Depending on the light source, the sample may be irradiated from adistance which varies such as 0.01 mm or more, such as 0.05 mm or more,such as 0.1 mm or more, such as 0.5 mm or more, such as 1 mm or more,such as 2.5 mm or more, such as 5 mm or more, such as 10 mm or more,such as 15 mm or more, such as 25 mm or more and including 50 mm ormore. Also, the angle or irradiation may also vary, ranging from 10° to90°, such as from 15° to 85°, such as from 20° to 80°, such as from 25°to 75° and including from 30° to 60°, for example at a 90° angle.

In practicing the subject methods, light from the irradiated sample ismeasured, such as by collecting light from the sample over a range ofwavelengths (e.g., 200 nm 1000 nm). In embodiments, methods may includeone or more of measuring light absorption by the sample (e.g.,brightfield light data), measuring light scatter (e.g., forward or sidescatter light data) and measuring light emission by the sample (e.g.,fluorescence light data).

Light from the sample may be measured at one or more wavelengths of,such as at 5 or more different wavelengths, such as at 10 or moredifferent wavelengths, such as at 25 or more different wavelengths, suchas at 50 or more different wavelengths, such as at 100 or more differentwavelengths, such as at 200 or more different wavelengths, such as at300 or more different wavelengths and including measuring the collectedlight at 400 or more different wavelengths.

Light may be collected over one or more of the wavelength ranges of 200nm 1200 nm. In some instances, methods include measuring the light fromthe sample over a range of wavelengths, such as from 200 nm to 1200 nm,such as from 300 nm to 1100 nm, such as from 400 nm to 1000 nm, such asfrom 500 nm to 900 nm and including from 600 nm to 800 nm. In otherinstances, methods include measuring collected light at one or morespecific wavelengths. For example, the collected light may be measuredat one or more of 450 nm, 518 nm, 519 nm, 561 nm, 578 nm, 605 nm, 607nm, 625 nm, 650 nm, 660 nm, 667 nm, 670 nm, 668 nm, 695 nm, 710 nm, 723nm, 780 nm, 785 nm, 647 nm, 617 nm and any combinations thereof. Incertain embodiments, methods including measuring wavelengths of lightwhich correspond to the fluorescence peak wavelength of certainfluorophores.

The collected light may be measured continuously or in discreteintervals. In some instances, methods include taking measurements of thelight continuously. In other instances, the light is measured indiscrete intervals, such as measuring light every 0.001 millisecond,every 0.01 millisecond, every 0.1 millisecond, every 1 millisecond,every 10 milliseconds, every 100 milliseconds and including every 1000milliseconds, or some other interval.

Measurements of the collected light may be taken one or more timesduring the subject methods, such as 2 or more times, such as 3 or moretimes, such as 5 or more times and including 10 or more times. Incertain embodiments, light from the sample is measured 2 or more times,with the data in certain instances being averaged.

In some embodiments, methods include further adjusting the light fromthe sample before detecting the light. For example, the light from thesample source may be passed through one or more lenses, mirrors,pinholes, slits, gratings, light refractors, and any combinationthereof. In some instances, the collected light is passed through one ormore focusing lenses, such as to reduce the profile of the light. Inother instances, the emitted light from the sample is passed through oneor more collimators to reduce light beam divergence.

In certain embodiments, methods include irradiating the sample with twoor more beams of frequency shifted light. As described above, a lightbeam generator component may be employed having a laser and anacousto-optic device for frequency shifting the laser light. In theseembodiments, methods include irradiating the acousto-optic device withthe laser. Depending on the desired wavelengths of light produced in theoutput laser beam (e.g., for use in irradiating a sample in a flowstream), the laser may have a specific wavelength that varies from 200nm to 1500 nm, such as from 250 nm to 1250 nm, such as from 300 nm to1000 nm, such as from 350 nm to 900 nm and including from 400 nm to 800nm. The acousto-optic device may be irradiated with one or more lasers,such as 2 or more lasers, such as 3 or more lasers, such as 4 or morelasers, such as 5 or more lasers and including 10 or more lasers. Thelasers may include any combination of types of lasers. For example, insome embodiments, the methods include irradiating the acousto-opticdevice with an array of lasers, such as an array having one or more gaslasers, one or more dye lasers and one or more solid-state lasers.

Where more than one laser is employed, the acousto-optic device may beirradiated with the lasers simultaneously or sequentially, or acombination thereof. For example, the acousto-optic device may besimultaneously irradiated with each of the lasers. In other embodiments,the acousto-optic device is sequentially irradiated with each of thelasers. Where more than one laser is employed to irradiate theacousto-optic device sequentially, the time each laser irradiates theacousto-optic device may independently be 0.001 microseconds or more,such as 0.01 microseconds or more, such as 0.1 microseconds or more,such as 1 microsecond or more, such as 5 microseconds or more, such as10 microseconds or more, such as 30 microseconds or more and including60 microseconds or more. For example, methods may include irradiatingthe acousto-optic device with the laser for a duration which ranges from0,001 microseconds to 100 microseconds, such as from 0.01 microsecondsto 75 microseconds, such as from 0.1 microseconds to 50 microseconds,such as from 1 microsecond to 25 microseconds and including from 5microseconds to 10 microseconds. In embodiments where the acousto-opticdevice is sequentially irradiated with two or more lasers, the durationthe acousto-optic device is irradiated by each laser may be the same ordifferent.

The time period between irradiation by each laser may also vary, asdesired, being separated independently by a delay of 0.001 microsecondsor more, such as 0.01 microseconds or more, such as 0.1 microseconds ormore, such as 1 microsecond or more, such as 5 microseconds or more,such as by 10 microseconds or more, such as by 15 microseconds or more,such as by 30 microseconds or more and including by 60 microseconds ormore. For example, the time period between irradiation by each lightsource may range from 0.001 microseconds to 60 microseconds, such asfrom 0.01 microseconds to 50 microseconds, such as from 0.1 microsecondsto 35 microseconds, such as from 1 microsecond to 25 microseconds andincluding from 5 microseconds to 10 microseconds. In certainembodiments, the time period between irradiation by each laser is 10microseconds. In embodiments where the acousto-optic device issequentially irradiated by more than two (i.e., 3 or more) lasers, thedelay between irradiation by each laser may be the same or different.

The acousto-optic device may be irradiated continuously or in discreteintervals. In some instances, methods include irradiating theacousto-optic device with the laser continuously. In other instances,the acousto-optic device is irradiated with the laser in discreteintervals, such as irradiating every 0.001 millisecond, every 0.01millisecond, every 0.1 millisecond, every 1 millisecond, every 10milliseconds, every 100 milliseconds and including every 1000milliseconds, or some other interval.

Depending on the laser, the acousto-optic device may be irradiated froma distance which varies such as 0.01 mm or more, such as 0.05 mm ormore, such as 0.1 mm or more, such as 0.5 mm or more, such as 1 mm ormore, such as 2.5 mm or more, such as 5 mm or more, such as 10 mm ormore, such as 15 mm or more, such as 25 mm or more and including 50 mmor more. Also, the angle or irradiation may also vary, ranging from 10°to 90°, such as from 15° to 85°, such as from 20° to 80°, such as from25° to 75° and including from 30° to 60°, for example at a 90° angle.

In embodiments, methods include applying radiofrequency drive signals tothe acousto-optic device to generate angularly deflected laser beams.Two or more radiofrequency drive signals may be applied to theacousto-optic device to generate an output laser beam with the desirednumber of angularly deflected laser beams, such as 3 or moreradiofrequency drive signals, such as 4 or more radiofrequency drivesignals, such as 5 or more radiofrequency drive signals, such as 6 ormore radiofrequency drive signals, such as 7 or more radiofrequencydrive signals, such as 8 or more radiofrequency drive signals, such as 9or more radiofrequency drive signals, such as 10 or more radiofrequencydrive signals, such as 15 or more radiofrequency drive signals, such as25 or more radiofrequency drive signals, such as 50 or moreradiofrequency drive signals and including 100 or more radiofrequencydrive signals.

The angularly deflected laser beams produced by the radiofrequency drivesignals each have an intensity based on the amplitude of the appliedradiofrequency drive signal. In some embodiments, methods includeapplying radiofrequency drive signals having amplitudes sufficient toproduce angularly deflected laser beams with a desired intensity. Insome instances, each applied radiofrequency drive signal independentlyhas an amplitude from about 0.001 V to about 500 V, such as from about0.005 V to about 400 V, such as from about 0.01 V to about 300 V, suchas from about 0.05 V to about 200 V, such as from about 0.1 V to about100 V, such as from about 0.5 V to about 75 V, such as from about 1 V to50 V, such as from about 2 V to 40 V, such as from 3 V to about 30 V andincluding from about 5 V to about 25 V. Each applied radiofrequencydrive signal has, in some embodiments, a frequency of from about 0.001MHz to about 500 MHz, such as from about 0.005 MHz to about 400 MHz,such as from about 0.01 MHz to about 300 MHz, such as from about 0.05MHz to about 200 MHz, such as from about 0.1 MHz to about 100 MHz, suchas from about 0.5 MHz to about 90 MHz, such as from about 1 MHz to about75 MHz, such as from about 2 MHz to about 70 MHz, such as from about 3MHz to about 65 MHz, such as from about 4 MHz to about 60 MHz andincluding from about 5 MHz to about 50 MHz.

In these embodiments, the angularly deflected laser beams in the outputlaser beam are spatially separated. Depending on the appliedradiofrequency drive signals and desired irradiation profile of theoutput laser beam, the angularly deflected laser beams may be separatedby 0.001 μm or more, such as by 0.005 μm or more, such as by 0.01 μm ormore, such as by 0.05 μm or more, such as by 0.1 μm or more, such as by0.5 μm or more, such as by 1 μm or more, such as by 5 μm or more, suchas by 10 or more, such as by 100 μm or more, such as by 500 μm or more,such as by 1000 μm or more and including by 5000 μm or more. In someembodiments, the angularly deflected laser beams overlap, such as withan adjacent angularly deflected laser beam along a horizontal axis ofthe output laser beam. The overlap between adjacent angularly deflectedlaser beams (such as overlap of beam spots) may be an overlap of 0.001μm or more, such as an overlap of 0.005 μm or more, such as an overlapof 0.01 μm or more, such as an overlap of 0.05 μm or more, such as anoverlap of 0.1 μm or more, such as an overlap of 0.5 μm or more, such asan overlap of 1 μm or more, such as an overlap of 5 μm or more, such asan overlap of 10 μm or more and including an overlap of 100 μm or more.

In certain instances, the flow stream is irradiated with a plurality ofbeams of frequency-shifted light and a cell in the flow stream is imagedby fluorescence imaging using radiofrequency tagged emission (FIRE) togenerate a frequency-encoded image, such as those described in Diebold,et al. Nature Photonics Vol, 7(10); 806-810 (2013) as well as describedin U.S. Pat. Nos. 9,423,353; 9,784,661 and 10,006,852 and U.S. PatentPublication Nos. 2017/0133857 and 2017/0350803, the disclosures of whichare herein incorporated by reference.

In embodiments, methods include generating spatial data of an object inthe flow stream from the detected light. The spatial data of the objectmay be generated from detected light absorption, detected light scatter,detected light emission or any combination thereof. In some instances,the spatial data of the object is generated from light absorptiondetected from the sample, such as from a brightfield light detector. Inother instances, the spatial data of the object is generated from lightscatter detected from the sample, such as from a side scatter detector,a forward scatter detector or a combination of a side scatter detectorand forward scatter detector. In yet other instances, the spatial dataof the object is generated from emitted light from the sample, such aslight from fluorophores added to the sample. In still other instances,the spatial data of the object is generated from a combination ofdetected light absorption, detected light scatter and detected lightemission.

In certain embodiments, the spatial data is calculated fromfrequency-encoded data (e.g., frequency-encoded fluorescence data). Inthese embodiments, the frequency-encoded data is generated by detectinglight from an object irradiated with a plurality of frequency shiftedbeams of light and a local oscillator beam. In one example, a pluralityof positions across (a horizontal axis) the flow stream are irradiatedby a laser beam that includes a local oscillator beam and a plurality ofradiofrequency-shifted laser beams such that different locations acrossthe flow stream are irradiated by the local oscillator beam and one ofthe radiofrequency-shifted beams. In some instances, the localoscillator is a frequency-shifted beam of light from a laser. In thisexample, each spatial location across the particle in the flow stream ischaracterized by a different beat frequency which corresponds to thedifference between the frequency of the local oscillator beam and thefrequency of the radiofrequency-shifted beam at that location. In someembodiments, frequency-encoded data from the object includes spatiallyencoded beat frequencies across a horizontal axis of the particle in theflow stream.

In embodiments, the spatial data may be calculated from thefrequency-encoded data by performing a transform of frequency-encodeddata. In one example, the spatial data is calculated by performing aFourier transform (FT) of the frequency-encoded data. In anotherexample, the spatial data is calculated by performing a discrete Fouriertransform (DFT) of the frequency-encoded data. In yet another example,the spatial data is calculated by performing a short time Fouriertransform (STFT) of the frequency-encoded data. In still anotherexample, the spatial data is calculated with a digital lock-in amplifierto heterodyne and de-multiplex the frequency-encoded data.

In some embodiments, methods include generating an image of an object inthe flow stream from the detected light. The image of the object may begenerated from detected light absorption, detected light scatter,detected light emission or any combination thereof. In some instances,the image of the object is generated from light absorption detected fromthe sample, such as from a brightfield light detector. In theseinstances, the image of the object is generated based on brightfieldimage data from the cell in the flow stream. In other instances, theimage of the object is generated from light scatter detected from thesample, such as from a side scatter detector, a forward scatter detectoror a combination of a side scatter detector and forward scatterdetector. In these instances, the image of the object is generated basedon scattered light image data. In yet other instances, the image of theobject is generated from emitted light from the sample, such as lightfrom fluorophores added to the sample. In these instances, the image ofthe object is generated based on fluorescent image data (i.e., imagingdata from fluorescent compounds on or in the cell). In still otherinstances, the image of the object is generated from a combination ofdetected light absorption, detected light scatter and detected lightemission.

One or more images of the object may be generated from the detectedlight. In some embodiments, a single image is generated from each formof detected light. For example, a first image of the object is generatedfrom detected light absorption; a second image of the object isgenerated from detected light scatter and a third image of the object isgenerated from detected light emission. In other embodiments, two ormore images are generated from each form of detected light, such as 3 ormore, such as 4 or more, such as 5 or more and including 10 or moreimages or a combination thereof.

In certain embodiments, methods include generating an image mask of theobject in the flow stream. In these embodiments, the image mask may befirst generated from a greyscale image of the object in the flow stream.The term “greyscale” is used herein in its conventional sense to referto images of the object in the flow stream that are composed of varyingshades of gray that are based on the intensity of light at each pixel.In embodiments, a pixel intensity threshold is determined from thegreyscale image where the pixel intensity threshold value is used toconvert each pixel into a binary value that is used to generate theimage mask of the object, as described in greater detail below. In someembodiments, the pixel intensity threshold is determined by minimizingthe intra-class variance of the greyscale image and calculating a pixelintensity threshold that is based on the minimized intra-class variance.In some embodiments, the pixel intensity threshold is determined with analgorithm where the detected light data includes two classes of pixelsfollowing a bimodal histogram (having foreground pixels and backgroundpixels), calculating an optimum threshold separating the two classes sothat their combined intra-class variance is minimal. In otherembodiments, methods include calculating an optimum threshold separatingthe two classes so that their inter-class variance is maximum.

In generating the image mask, each pixel in the greyscale image of theobject is compared against the determined intensity threshold value andconverted to a binary pixel value. Each pixel in the greyscale image ofthe object may be compared against the determined intensity thresholdvalue in any order as desired. In some embodiments, pixels along eachhorizontal row in the greyscale image of the object are compared againstthe determined intensity threshold value. In some instances, each pixelis compared against the determined intensity threshold value from theleft side of the greyscale image of the object to the right side of thegreyscale image of the object. In other instances, each pixel iscompared against the determined intensity threshold value from the rightside of the greyscale image of the object to the left side of thegreyscale image of the object. In other embodiments, pixels along eachvertical column in the greyscale image of the object are comparedagainst the determined intensity threshold value. In some instances,each pixel is compared against the determined intensity threshold valuefrom the top of the greyscale image of the object to the bottom of thegreyscale image of the object along each vertical column. In otherinstances, each pixel is compared against the determined intensitythreshold value from the bottom of the greyscale image of the object tothe top of the greyscale image of the object along each vertical column.

Depending on the size of the object in the flow stream being imaged andthe optics used to collect the light from the sample (described ingreater detail below), all of part of the pixels in the greyscale imageof the object may be compared against the intensity threshold value. Forexample, in practicing the subject methods 50% or more of the pixels inthe greyscale image of the object may be compared against the intensitythreshold value, such as 60% or more, such as 70% or more, such as 80%or more, such as 90% or more, such as 95% or more, such as 97% or moreand including 99% or more of the pixels in the greyscale image of theobject. In certain embodiments, all (100%) of the pixels in thegreyscale image of the object are compared against the intensitythreshold value.

As summarized above, each pixel in the greyscale image of the object isconverted to a binary pixel value. Depending on the type of lightdetected, each pixel is assigned a binary pixel value of 1 or a binarypixel value of 0. In one example, methods include detecting lightabsorption (e.g., brightfield image data) from the flow stream andassigning a binary pixel value of 1 to each pixel in the greyscale imageof the object when the pixel intensity is less than the intensitythreshold value and assigning a binary pixel value of 0 when the pixelintensity of the greyscale image of the object is greater than theintensity threshold value. In another example, methods include detectinglight scatter from the object in the flow stream and assigning a binarypixel value of 1 to each pixel in the greyscale image when the pixelintensity is greater than the intensity threshold value and assigning abinary pixel value of 0 when the pixel intensity is less than theintensity threshold value. In yet another example, methods includedetecting fluorescence from the object in the flow stream and assigninga binary pixel value of 1 to each pixel in the greyscale image of theobject when the pixel intensity is greater than the intensity thresholdvalue and assigning a binary pixel value of 0 when the pixel intensityis less than the intensity threshold value.

Where a binary pixel value is assigned to each pixel in the greyscaleimage of the object across a horizontal row, in some embodiments methodsfurther include determining the first pixel across the horizontal rowhaving a binary pixel value of 1 and determining the last pixel in thehorizontal row having a binary pixel value of 1. In one example, methodsinclude determining the first pixel from the left side of the horizontalrow having an assigned binary pixel value of 1 and determining the lastpixel from the left side of horizontal row having an assigned binarypixel value of 1. In another example, methods include determining thefirst pixel from the right side of the horizontal row having an assignedbinary pixel value of 1 and determining the last pixel from the rightside of horizontal row having an assigned binary pixel value of 1. Inother embodiments, methods further include determining the first pixelacross the horizontal row having a binary pixel value of 0 anddetermining the last pixel in the horizontal row having a binary pixelvalue of 0. In one example, methods include determining the first pixelfrom the left side of the horizontal row having an assigned binary pixelvalue of 0 and determining the last pixel from the left side ofhorizontal row having an assigned binary pixel value of 0. In anotherexample, methods include determining the first pixel from the right sideof the horizontal row having an assigned binary pixel value of 0 anddetermining the last pixel from the right side of horizontal row havingan assigned binary pixel value of 0.

Where a binary pixel value is assigned to each pixel in the greyscaleimage of the object along a vertical column, in some embodiments methodsfurther include determining the first pixel along the vertical columnhaving a binary pixel value of 1 and determining the last pixel alongthe vertical column having a binary pixel value of 1. In one example,methods include determining the first pixel from the top of the verticalcolumn having an assigned binary pixel value of 1 and determining thelast pixel from the top of the vertical column having an assigned binarypixel value of 1. In another example, methods include determining thefirst pixel from the bottom of the vertical column having an assignedbinary pixel value of 1 and determining the last pixel from the bottomof the vertical column having an assigned binary pixel value of 1. Inother embodiments, methods further include determining the first pixelalong a vertical column having a binary pixel value of 0 and determiningthe last pixel in the vertical column having a binary pixel value of 0.In one example, methods include determining the first pixel from the topof the vertical column having an assigned binary pixel value of 0 anddetermining the last pixel from the top of the vertical column having anassigned binary pixel value of 0. In another example, methods includedetermining the first pixel from the bottom of the vertical columnhaving an assigned binary pixel value of 0 and determining the lastpixel from bottom of the vertical column having an assigned binary pixelvalue of 0.

In some embodiments, methods include assessing one or morecharacteristics of the object based on the spatial data. For example,methods may include determining the size of the object, the center ofmass of the object, the eccentricity of the object along a horizontalaxis or vertical axis or a combination thereof based on the spatialdata. In some instances, methods include assessing one or more of thesize, center of mass and eccentricity based on the spatial data of theobject and determining whether the object is a cell aggregate. Incertain instances, the object is determined to be a cell aggregate basedon the size and center of mass of the object. In other instances, theobject is determined to be a cell aggregate based on the determined sizeand eccentricity of the object. In yet other instances, the object isdetermined to be a cell aggregate based on the determined center of massand eccentricity of the object. In still other instances, the object isdetermined to be a cell aggregate based on the determined size, centerof mass and eccentricity of the object.

In some embodiments, methods include assessing one or morecharacteristics of the object based on the generated image. For example,methods may include determining the size of the object, the center ofmass of the object, the eccentricity of the object along a horizontalaxis or vertical axis or a combination thereof based on the generatedimage. In some instances, methods include assessing one or more of thesize, center of mass and eccentricity based on the generated image ofthe object and determining whether the object is a cell aggregate. Incertain instances, the object is determined to be a cell aggregate basedon the size and center of mass of the object. In other instances, theobject is determined to be a cell aggregate based on the determined sizeand eccentricity of the object. In yet other instances, the object isdetermined to be a cell aggregate based on the determined center of massand eccentricity of the object. In still other instances, the object isdetermined to be a cell aggregate based on the determined size, centerof mass and eccentricity of the object.

In some embodiments, methods further include calculating an image momentof the object. The term “image moment” is used herein in itsconventional sense to refer to a weighted average of the spatial data orpixel intensities in an image (e.g., generated image or generated imagemask). As described below, the determined image moment may be used tocalculate total intensity of the pixels of the object, the total areaoccupied by object, the centroid (i.e., geometric center) of the objectas well as the orientation of the object (e.g., in the image or imagemask). In some embodiments, the image moment is calculated according to:

M _(m,n)=Σ(x−x )^(m)(y−y )^(n)Im(x,y)=M·Im(x,y)

where m is the image moment computed along the x-axis; and n is theimage moment computed along the y-axis. In some instances, methodsinclude calculating a first order image moment of the object along ahorizontal axis. In other instances, methods include calculating asecond order image moment of the object along a horizontal axis. In yetother instances, methods include calculating a first order image momentof the object along a vertical axis. In still other instances, methodsinclude calculating a second order image moment of the object along avertical axis.

In some embodiments, one or more properties of the object are determinedbased on the calculated image moment and spatial data. For example,methods may include determining the size of the cell, the center of massof the cell or the eccentricity of the cell based on the calculatedimage moment and spatial data. In these embodiments, methods includecalculating one or more image moments and then determining thecharacteristic of the cell using both the calculated image moment andthe spatial data.

In some embodiments, one or more properties of the object are determinedbased on the calculated image moment and generated image. For example,methods may include determining the size of the cell, the center of massof the cell or the eccentricity of the cell based on the calculatedimage moment and generated image of the object (e.g., image or imagemask). In these embodiments, methods include calculating one or moreimage moments and then determining the characteristic of the cell usingboth the calculated image moment and the image of the object.

In some instances, the center of mass may be calculated from the imagemoment and the generated image of the object. For example, the center ofmass of the object may be determined from the calculated image momentand the generated image according to:

${{Center}\mspace{14mu}{of}\mspace{14mu}{Mass}} = \frac{M_{1,0}}{M_{0,0}}$

In other instances, the orientation of the object may be calculated fromthe image moment and the generated image of the object. For example, theorientation of the object may be determined from the calculated imagemoment and the generated image according to:

${{Orientation}\mspace{14mu}{of}\mspace{14mu}{cell}} = {\frac{1}{2}\arctan\frac{2\left( {M_{1,1} - \frac{M_{1,0}M_{0,1}}{M_{0,0}}} \right)}{M_{2,0} - \frac{M_{2,0}^{2}}{M_{0,0}} - M_{0,2} + \frac{M_{0,2}^{2}}{M_{0,0}}}}$

In still other instances, the eccentricity of the object may becalculated from the image moment and the generated image of the object.For example, the eccentricity of the object may be determined from thecalculated image moment and the generated image according to:

${Eccentricity} = \frac{\begin{matrix}{M_{2,0} - \frac{M_{2,0}^{2}}{M_{0,0}^{2}} + M_{0,2} - \frac{M_{0,2}^{2}}{M_{0,0}^{2}} -} \\\sqrt{{4\left( {M_{1,1} - \frac{M_{1,0}M_{0,1}}{M_{0,0}^{2}}} \right)^{2}} + \left( {M_{2,0} - \frac{M_{2,0}^{2}}{M_{0,0}} - M_{0,2} + \frac{M_{0,2}^{2}}{M_{0,0}}} \right)^{2}}\end{matrix}}{\begin{matrix}{M_{2,0} - \frac{M_{2,0}^{2}}{M_{0,0}^{2}} + M_{0,2} - \frac{M_{0,2}^{2}}{M_{0,0}^{2}} +} \\\sqrt{{4\left( {M_{1,1} - \frac{M_{1,0}M_{0,1}}{M_{0,0}^{2}}} \right)^{2}} + \left( {M_{2,0} - \frac{M_{2,0}^{2}}{M_{0,0}} - M_{0,2} + \frac{M_{0,2}^{2}}{M_{0,0}}} \right)^{2}}\end{matrix}}$

In some embodiments, methods include assessing one or more of: 1) thecalculated size of the object; 2) the center of mass of the object; and3) the eccentricity of the object and identifying whether the object isan aggregate of particles (e.g., a cell aggregate) or a single particle(e.g., a single cell). In some instances, the object is determined to bean aggregate based on the calculated size of the object. In one example,the calculated size of the object is compared to a predetermined sizefor cells of interest to classify the object as being a single particle(e.g., single cell) or an aggregate. In this example, the predeterminedsize may be determined using standard calibration sample or referencesize data. In another example, the calculated size of the object iscompared to a threshold value such that where the object is determinedto have a size greater than the threshold value, the object isclassified as being an aggregate.

In other instances, the object is determined to be an aggregate based onthe calculated center of mass of the object. In one example, thecalculated center of mass of the object is compared to a predeterminedcenter of mass for cells of interest to classify the object as being asingle particle (e.g., single cell) or an aggregate. In this example,the predetermined center of mass may be determined using standardcalibration sample or reference center of mass data of the cells ofinterest.

In yet other instances, the object is determined to be an aggregatebased on the calculated eccentricity of the object. In one example, thecalculated eccentricity of the object is compared to a predeterminedeccentricity for cells of interest to classify the object as being asingle particle (e.g., single cell) or an aggregate. In this example,the predetermined eccentricity may be determined using standardcalibration sample or reference eccentricity data of the cells ofinterest.

In certain instances, the object is identified to be an aggregate basedon the determined size and center of mass of the object. In otherinstances, the object is identified to be an aggregate based on thedetermined size and eccentricity of the object. In yet other instances,the object is identified to be an aggregate based on the determinedcenter of mass and eccentricity of the object. In still other instances,the object is identified to be an aggregate based on the determinedsize, center of mass and eccentricity of the object.

In determining whether the object is an aggregate (e.g., cellaggregate), in certain instances, methods include: 1) assessing one ormore properties of the object based on the calculated image moment andspatial data; and 2) assessing a light scatter detector output signalfrom the object in the interrogation region of the flow stream. In theseembodiments, light scatter detector output signal may be a forward lightscatter detector output or a side light scatter detector output or acombination thereof. In embodiments, the light scatter detector outputsignal may be a signal pulse width, a signal pulse height and a signalpulse area or a combination thereof. In one example, methods includeassessing one or more properties of the object (size, center of mass,eccentricity) based on the calculated image moment and spatial data andassessing the pulse width of a light scatter detector output signal. Inanother example, methods include assessing one or more properties of theobject (size, center of mass, eccentricity) based on the calculatedimage moment and spatial data and assessing the pulse height of a lightscatter detector output signal. In yet another example, methods includeassessing one or more properties of the object (size, center of mass,eccentricity) based on the calculated image moment and spatial data andassessing the pulse area of a light scatter detector output signal.

In certain instances, methods include: 1) assessing one or moreproperties of the object based on the calculated image moment andgenerated image; and 2) assessing a light scatter detector output signalfrom the object in the interrogation region of the flow stream. In theseembodiments, light scatter detector output signal may be a forward lightscatter detector output or a side light scatter detector output or acombination thereof. In embodiments, the light scatter detector outputsignal may be a signal pulse width, a signal pulse height and a signalpulse area or a combination thereof. In one example, methods includeassessing one or more properties of the object (size, center of mass,eccentricity) based on the calculated image moment and generated imageand assessing the pulse width of a light scatter detector output signal.In another example, methods include assessing one or more properties ofthe object (size, center of mass, eccentricity) based on the calculatedimage moment and generated image and assessing the pulse height of alight scatter detector output signal. In yet another example, methodsinclude assessing one or more properties of the object (size, center ofmass, eccentricity) based on the calculated image moment and generatedimage and assessing the pulse area of a light scatter detector outputsignal.

In certain embodiments, methods include: 1) assessing each of the pulsewidth, the pulse height and pulse area of a light scatter detectoroutput signal, followed by 2) assessing one or more properties of theobject (size, center of mass, eccentricity) based on the calculatedimage moment and spatial data. In these embodiments, the light scatterdetector output signals may be collected from one or more of a sidescatter detector and a forward scatter detector. In some instances,light scatter detector output signals used in the subject methodsaccording to these embodiments are collected from a side scatterdetector. In other instances, light scatter detector output signals usedin the subject methods are collected from a forward scatter detector. Inyet other instances, light scatter detector output signals used in thesubject methods are collected from both a side scatter detector and aforward scatter detector.

In certain embodiments, methods include: 1) assessing each of the pulsewidth, the pulse height and pulse area of a light scatter detectoroutput signal, followed by 2) assessing one or more properties of theobject (size, center of mass, eccentricity) based on the calculatedimage moment and generated image. In these embodiments, the lightscatter detector output signals may be collected from one or more of aside scatter detector and a forward scatter detector. In some instances,light scatter detector output signals used in the subject methodsaccording to these embodiments are collected from a side scatterdetector. In other instances, light scatter detector output signals usedin the subject methods are collected from a forward scatter detector. Inyet other instances, light scatter detector output signals used in thesubject methods are collected from both a side scatter detector and aforward scatter detector.

In some instances, methods include determining that the object is ahorizontal cell aggregate where two or more cells are aligned togetheracross a horizontal axis of the flow stream, such as 3 or more cells,such as 4 or more cells and including 5 or more cells. In otherinstances, methods include determining that the object is a verticalcell aggregate where two or more cells are aligned together along avertical axis (i.e., longitudinal axis) of the flow stream, such as 3 ormore cells, such as 4 or more cells and including 5 or more cells. Inyet other instances, methods include determining that the object iscombination cell aggregate having two or more cells aligned togetheracross a horizontal axis and having two or more cells aligned togetheralong a vertical axis.

FIGS. 1A and 1B depict images of imaged cell aggregates according tocertain embodiments. FIG. 1A depicts a vertically oriented cellaggregate having two cells aligned together along a vertical axis (i.e.,along the longitudinal axis of the flow stream). In some embodiments,the object in FIG. 1A can be identified as a vertically aligned cellaggregate based on a calculated second order image moment along they-axis. FIG. 1B depicts a horizontally oriented cell aggregate havingtwo cells aligned together along a horizontal axis (i.e., orthogonal tothe longitudinal axis of the flow stream). In some embodiments, theobject in FIG. 1B can be identified as a horizontally aligned cellaggregate based on a calculated second order image moment along thex-axis.

FIGS. 2A and 2B depicts the use of images and calculated eccentricity todiscriminate between a single cell and an aggregate of cells accordingto certain embodiments. FIG. 2A depicts a single cell and FIG. 2Bdepicts an aggregate of 4 cells. Based on light scatter detector outputsignals, the single cell in FIG. 2A and the cell aggregate in FIG. 2Bexhibit similar output signal parameters (e.g., output signal pulsewidth, pulse height and pulse area). By generating the images depictedin FIGS. 2A and 2B and calculating the eccentricity, the object in FIG.2A can be correctly identified as being a single cell according to thesubject methods and the object in FIG. 2B can be correctly identified asbeing a cell aggregate.

FIG. 3 depicts a flow chart for imaging and characterizing an object ina flow stream according to certain embodiments. At step 301, light(light absorption, scattered light or emission) from an object in theflow stream are detected. At step 302, spatial data of the object isgenerated. At step 303, an image (e.g., a greyscale image) of the objectis generated. At step 304, a pixel intensity threshold is determinedbased on the pixels from the image. In certain embodiments, at step 305,each pixel in the image is converted to a binary pixel value bycomparing the intensity of each pixel against the determined pixelintensity threshold. An image mask is then generated using the binarypixel values at step 306. One or more properties of the imaged object isdetermined from the spatial data, generated image (or image mask) atstep 307. Based on the determined properties, the object is identifiedas being an aggregate (e.g., a cell aggregate) or a single particle(e.g., a single cell) at step 308. In some instances, light scatterdetector output signals (pulse width, pulse height and pulse area) areassessed (step 309) and are also used to identify the object.

As summarized above, methods of the present disclosure also includesorting the object based on the spatial data, generated image, generatedimage mask, a calculated image moment, one or more determined propertiesof the object (e.g., size, center of mass, eccentricity) or acombination thereof. The term “sorting” is used herein in itsconventional sense to refer to separating components (e.g., dropletscontaining cells, droplets containing non-cellular particles such asbiological macromolecules) of a sample and in some instances, deliveringthe separated components to one or more sample collection containers.For example, methods may include sorting 2 or more components of thesample, such as 3 or more components, such as 4 or more components, suchas 5 or more components, such as 10 or more components, such as 15 ormore components and including sorting 25 or more components of thesample. In some embodiments, the object is identified as being a singlecell and is sorted to a first sample component collection location. Inother embodiments, the object is identified as being a cell aggregateand is sorted to a second sample component collection location. In someinstances, the first sample component collection location includes asample collection container and the second sample component collectionlocation includes a waste collection container.

In sorting the object from the sample in the flow stream, methodsinclude data acquisition, analysis and recording, such as with acomputer, where multiple data channels record data from each detectorused in generating the image or image mask of the object (e.g., scatterdetectors, brightfield photodetectors or fluorescence detectors). Inthese embodiments, analysis includes classifying and counting particlessuch that each particle is present as a set of digitized parametervalues. The subject systems (described below) may be set to trigger on aselected parameter in order to distinguish the particles of interestfrom background and noise.

A particular subpopulation of interest (e.g., single cells) may thenfurther analyzed by “gating” based on the data collected for the entirepopulation. To select an appropriate gate, the data is plotted so as toobtain the best separation of subpopulations possible. This proceduremay be performed by plotting image moment or one or more of thedetermined properties (e.g., size, center of mass, eccentricity). Inother embodiments, methods include plotting forward light scatter (FSC)vs. side (i.e., orthogonal) light scatter (SSC) on a two-dimensional dotplot. In yet other embodiments, methods include plotting one or more ofthe determined properties (e.g., size, center of mass, eccentricity)against one or more of forward light scatter (FSC) and side (i.e.,orthogonal) light scatter (SSC). In still other embodiments, methodsinclude gating the population of particles for forward light scatter(FSC) and side (i.e., orthogonal) light scatter (SSC), followed bygating based on one or more of the determined properties (e.g., size,center of mass, eccentricity) based on the image of the object. In stillother embodiments, methods include gating the population of particlesbased on one or more of the determined properties (e.g., size, center ofmass, eccentricity) based on the image of the object, followed by gatingthe population of particles for forward light scatter (FSC) and side(i.e., orthogonal) light scatter (SSC). In yet other embodiments,methods include assessing the forward light scatter (FSC) and side(i.e., orthogonal) light scatter (SSC) output signals and the one ormore of the determined properties (e.g., size, center of mass,eccentricity) based on the image of the object and classifying theobject as being a single particle (e.g., single cell) or an aggregate(e.g., cell aggregate), followed by gating based on the classificationof the object.

A subpopulation of objects is then selected (i.e., those single cellswithin the gate) and particles that are not within the gate areexcluded. Where desired, the gate may be selected by drawing a linearound the desired subpopulation using a cursor on a computer screen.Only those particles within the gate are then further analyzed byplotting the other parameters for these particles, such as fluorescence.Where desired, the above analysis may be configured to yield counts ofthe particles of interest in the sample.

In some embodiments, methods for sorting components of sample includesorting particles (e.g., cells in a biological sample) with particlesorting module having deflector plates, such as described in U.S. PatentPublication No. 2017/0299493, filed on Mar. 28, 2017, the disclosure ofwhich is incorporated herein by reference. In certain embodiments, cellsof the sample are sorted using a sort decision module having a pluralityof sort decision units, such as those described in U.S. ProvisionalPatent Application No. 62/803,264, filed on Feb. 8, 2019, the disclosureof which is incorporated herein by reference.

Systems for Characterizing Particles in a Sample

As summarized above, aspects of the present disclosure include a systemfor characterizing particles of a sample (e.g., cells in a biologicalsample). Systems according to certain embodiments include a light sourceconfigured to irradiate a sample having cells in a flow stream, a lightdetection system having a photodetector and a processor having memoryoperably coupled to the processor such that the memory includesinstructions stored thereon, which when executed by the processor, causethe processor to generate spatial data of an object in the flow streamin an interrogation region and to determine whether the object in theflow stream is a cell aggregate based on the spatial data.

Systems of interest include a light source configured to irradiate asample in a flow stream. In embodiments, the light source may be anysuitable broadband or narrow band source of light. Depending on thecomponents in the sample (e.g., cells, beads, non-cellular particles,etc.), the light source may be configured to emit wavelengths of lightthat vary, ranging from 200 nm to 1500 nm, such as from 250 nm to 1250nm, such as from 300 nm to 1000 nm, such as from 350 nm to 900 nm andincluding from 400 nm to 800 nm. For example, the light source mayinclude a broadband light source emitting light having wavelengths from200 nm to 900 nm. In other instances, the light source includes a narrowband light source emitting a wavelength ranging from 200 nm to 900 nm.For example, the light source may be a narrow band LED (1 nm-25 nm)emitting light having a wavelength ranging between 200 nm to 900 nm.

In some embodiments, the light source is a laser. Lasers of interest mayinclude pulsed lasers or continuous wave lasers. For example, the lasermay be a gas laser, such as a helium-neon laser, argon laser, kryptonlaser, xenon laser, nitrogen laser, CO₂ laser, CO laser, argon-fluorine(ArF) excimer laser, krypton-fluorine (KrF) excimer laser, xenonchlorine (XeCl) excimer laser or xenon-fluorine (XeF) excimer laser or acombination thereof; a dye laser, such as a stilbene, coumarin orrhodamine laser; a metal-vapor laser, such as a helium-cadmium (HeCd)laser, helium-mercury (HeHg) laser, helium-selenium (HeSe) laser,helium-silver (HeAg) laser, strontium laser, neon-copper (NeCu) laser,copper laser or gold laser and combinations thereof; a solid-statelaser, such as a ruby laser, an Nd:YAG laser, NdCrYAG laser, Er:YAGlaser, Nd:YLF laser, Nd:YVO₄ laser, Nd:YCa₄O(BO₃)₃ laser, Nd:YCOB laser,titanium sapphire laser, thulim YAG laser, ytterbium YAG laser,ytterbium₂O₃ laser or cerium doped lasers and combinations thereof; asemiconductor diode laser, optically pumped semiconductor laser (OPSL),or a frequency doubled- or frequency tripled implementation of any ofthe above mentioned lasers.

In other embodiments, the light source is a non-laser light source, suchas a lamp, including but not limited to a halogen lamp, deuterium arclamp, xenon arc lamp, a light-emitting diode, such as a broadband LEDwith continuous spectrum, superluminescent emitting diode, semiconductorlight emitting diode, wide spectrum LED white light source, an multi-LEDintegrated. In some instances the non-laser light source is a stabilizedfiber-coupled broadband light source, white light source, among otherlight sources or any combination thereof.

In certain embodiments, the light source is a light beam generator thatis configured to generate two or more beams of frequency shifted light.In some instances, the light beam generator includes a laser, aradiofrequency generator configured to apply radiofrequency drivesignals to an acousto-optic device to generate two or more angularlydeflected laser beams. In these embodiments, the laser may be a pulsedlasers or continuous wave laser. For example lasers in light beamgenerators of interest may be a gas laser, such as a helium-neon laser,argon laser, krypton laser, xenon laser, nitrogen laser, CO2 laser, COlaser, argon-fluorine (ArF) excimer laser, krypton-fluorine (KrF)excimer laser, xenon chlorine (XeCl) excimer laser or xenon-fluorine(XeF) excimer laser or a combination thereof; a dye laser, such as astilbene, coumarin or rhodamine laser: a metal-vapor laser, such as ahelium-cadmium (HeCd) laser, helium-mercury (HeHg) laser,helium-selenium (HeSe) laser, helium-silver (HeAg) laser, strontiumlaser, neon-copper (NeCu) laser, copper laser or gold laser andcombinations thereof; a solid-state laser, such as a ruby laser, anNd:YAG laser, NdCrYAG laser, Er:YAG laser, Nd:YLF laser, Nd:YVO4 laser,Nd:YCa4O(BO3)3 laser, Nd:YCOB laser, titanium sapphire laser, thulim YAGlaser, ytterbium YAG laser, ytterbium2O3 laser or cerium doped lasersand combinations thereof.

The acousto-optic device may be any convenient acousto-optic protocolconfigured to frequency shift laser light using applied acoustic waves.In certain embodiments, the acousto-optic device is an acousto-opticdeflector. The acousto-optic device in the subject system is configuredto generate angularly deflected laser beams from the light from thelaser and the applied radiofrequency drive signals. The radiofrequencydrive signals may be applied to the acousto-optic device with anysuitable radiofrequency drive signal source, such as a direct digitalsynthesizer (DDS), arbitrary waveform generator (AWG), or electricalpulse generator.

In embodiments, a controller is configured to apply radiofrequency drivesignals to the acousto-optic device to produce the desired number ofangularly deflected laser beams in the output laser beam, such as beingconfigured to apply 3 or more radiofrequency drive signals, such as 4 ormore radiofrequency drive signals, such as 5 or more radiofrequencydrive signals, such as 6 or more radiofrequency drive signals, such as 7or more radiofrequency drive signals, such as 8 or more radiofrequencydrive signals, such as 9 or more radiofrequency drive signals, such as10 or more radiofrequency drive signals, such as 15 or moreradiofrequency drive signals, such as 25 or more radiofrequency drivesignals, such as 50 or more radiofrequency drive signals and includingbeing configured to apply 100 or more radiofrequency drive signals.

In some instances, to produce an intensity profile of the angularlydeflected laser beams in the output laser beam, the controller isconfigured to apply radiofrequency drive signals having an amplitudethat varies such as from about 0.001 V to about 500 V, such as fromabout 0.005 V to about 400 V, such as from about 0.01 V to about 300 V,such as from about 0.05 V to about 200 V, such as from about 0.1 V toabout 100 V, such as from about 0.5 V to about 75 V, such as from about1 V to 50 V, such as from about 2 V to 40 V, such as from 3 V to about30 V and including from about 5 V to about 25 V. Each appliedradiofrequency drive signal has, in some embodiments, a frequency offrom about 0.001 MHz to about 500 MHz, such as from about 0.005 MHz toabout 400 MHz, such as from about 0.01 MHz to about 300 MHz, such asfrom about 0.05 MHz to about 200 MHz, such as from about 0.1 MHz toabout 100 MHz, such as from about 0.5 MHz to about 90 MHz, such as fromabout 1 MHz to about 75 MHz, such as from about 2 MHz to about 70 MHz,such as from about 3 MHz to about 65 MHz, such as from about 4 MHz toabout 60 MHz and including from about 5 MHz to about 50 MHz.

In certain embodiments, the controller has a processor having memoryoperably coupled to the processor such that the memory includesinstructions stored thereon, which when executed by the processor, causethe processor to produce an output laser beam with angularly deflectedlaser beams having a desired intensity profile. For example, the memorymay include instructions to produce two or more angularly deflectedlaser beams with the same intensities, such as 3 or more, such as 4 ormore, such as 5 or more, such as 10 or more, such as 25 or more, such as50 or more and including memory may include instructions to produce 100or more angularly deflected laser beams with the same intensities. Inother embodiments, the may include instructions to produce two or moreangularly deflected laser beams with different intensities, such as 3 ormore, such as 4 or more, such as 5 or more, such as 10 or more, such as25 or more, such as 50 or more and including memory may includeinstructions to produce 100 or more angularly deflected laser beams withdifferent intensities.

In certain embodiments, the controller has a processor having memoryoperably coupled to the processor such that the memory includesinstructions stored thereon, which when executed by the processor, causethe processor to produce an output laser beam having increasingintensity from the edges to the center of the output laser beam alongthe horizontal axis. In these instances, the intensity of the angularlydeflected laser beam at the center of the output beam may range from0.1% to about 99% of the intensity of the angularly deflected laserbeams at the edge of the output laser beam along the horizontal axis,such as from 0.5% to about 95%, such as from 1% to about 90%, such asfrom about 2% to about 85%, such as from about 3% to about 80%, such asfrom about 4°/h to about 75%, such as from about 5°/h to about 70%, suchas from about 6% to about 65%, such as from about 7% to about 60%, suchas from about 8% to about 55% and including from about 10% to about 50%of the intensity of the angularly deflected laser beams at the edge ofthe output laser beam along the horizontal axis. In other embodiments,the controller has a processor having memory operably coupled to theprocessor such that the memory includes instructions stored thereon,which when executed by the processor, cause the processor to produce anoutput laser beam having an increasing intensity from the edges to thecenter of the output laser beam along the horizontal axis. In theseinstances, the intensity of the angularly deflected laser beam at theedges of the output beam may range from 0.1% to about 99% of theintensity of the angularly deflected laser beams at the center of theoutput laser beam along the horizontal axis, such as from 0.5% to about95%, such as from 1% to about 90%, such as from about 2% to about 85%,such as from about 3% to about 80%, such as from about 4% to about 75%,such as from about 5% to about 70%, such as from about 6% to about 65%,such as from about 7% to about 60%, such as from about 8% to about 55%and including from about 10% to about 50% of the intensity of theangularly deflected laser beams at the center of the output laser beamalong the horizontal axis. In yet other embodiments, the controller hasa processor having memory operably coupled to the processor such thatthe memory includes instructions stored thereon, which when executed bythe processor, cause the processor to produce an output laser beamhaving an intensity profile with a Gaussian distribution along thehorizontal axis. In still other embodiments, the controller has aprocessor having memory operably coupled to the processor such that thememory includes instructions stored thereon, which when executed by theprocessor, cause the processor to produce an output laser beam having atop hat intensity profile along the horizontal axis.

In embodiments, light beam generators of interest may be configured toproduce angularly deflected laser beams in the output laser beam thatare spatially separated. Depending on the applied radiofrequency drivesignals and desired irradiation profile of the output laser beam, theangularly deflected laser beams may be separated by 0.001 μm or more,such as by 0.005 μm or more, such as by 0.01 μm or more, such as by 0.05μm or more, such as by 0.1 μm or more, such as by 0.5 μm or more, suchas by 1 μm or more, such as by 5 μm or more, such as by 10 μm or more,such as by 100 μm or more, such as by 500 μm or more, such as by 1000 μmor more and including by 5000 μm or more. In some embodiments, systemsare configured to produce angularly deflected laser beams in the outputlaser beam that overlap, such as with an adjacent angularly deflectedlaser beam along a horizontal axis of the output laser beam. The overlapbetween adjacent angularly deflected laser beams (such as overlap ofbeam spots) may be an overlap of 0.001 μm or more, such as an overlap of0.005 μm or more, such as an overlap of 0.01 μm or more, such as anoverlap of 0.05 μm or more, such as an overlap of 0.1 μM or more, suchas an overlap of 0.5 μm or more, such as an overlap of 1 μm or more,such as an overlap of 5 μm or more, such as an overlap of 10 μm or moreand including an overlap of 100 μm or more.

In certain instances, light beam generators configured to generate twoor more beams of frequency shifted light include laser excitationmodules as described in U.S. Pat. Nos. 9,423,353; 9,784,661 and10,006,852 and U.S. Patent Publication Nos. 2017/0133857 and2017/0350803, the disclosures of which are herein incorporated byreference.

In embodiments, systems include a light detection system having one ormore photodetectors for detecting and measuring light from the sample.Photodetectors of interest may be configured to measure light absorption(e.g., for brightfield light data), light scatter (e.g., forward or sidescatter light data), light emission (e.g., fluorescence light data) fromthe sample or a combination thereof. Photodetectors of interest mayinclude, but are not limited to optical sensors, such as active-pixelsensors (APSs), avalanche photodiode, image sensors, charge-coupleddevices (CCDs), intensified charge-coupled devices (ICCDs), lightemitting diodes, photon counters, bolometers, pyroelectric detectors,photoresistors, photovoltaic cells, photodiodes, photomultiplier tubes,phototransistors, quantum dot photoconductors or photodiodes andcombinations thereof, among other photodetectors. In certainembodiments, light from a sample is measured with a charge-coupleddevice (CCD), semiconductor charge-coupled devices (CCD), active pixelsensors (APS), complementary metal-oxide semiconductor (CMOS) imagesensors or N-type metal-oxide semiconductor (NMOS) image sensors.

In some embodiments, light detection systems of interest include aplurality of photodetectors. In some instances, the light detectionsystem includes a plurality of solid-state detectors such asphotodiodes. In certain instances, the light detection system includes aphotodetector array; such as an array of photodiodes. In theseembodiments, the photodetector array may include 4 or morephotodetectors, such as 10 or more photodetectors, such as 25 or morephotodetectors, such as 50 or more photodetectors, such as 100 or morephotodetectors, such as 250 or more photodetectors, such as 500 or morephotodetectors, such as 750 or more photodetectors and including 1000 ormore photodetectors. For example, the detector may be a photodiode arrayhaving 4 or more photodiodes, such as 10 or more photodiodes, such as 25or more photodiodes, such as 50 or more photodiodes, such as 100 or morephotodiodes; such as 250 or more photodiodes, such as 500 or morephotodiodes, such as 750 or more photodiodes and including 1000 or morephotodiodes.

The photodetectors may be arranged in any geometric configuration asdesired, where arrangements of interest include, but are not limited toa square configuration, rectangular configuration, trapezoidalconfiguration, triangular configuration, hexagonal configuration,heptagonal configuration, octagonal configuration, nonagonalconfiguration, decagonal configuration, dodecagonal configuration,circular configuration, oval configuration as well as irregularpatterned configurations. The photodetectors in the photodetector arraymay be oriented with respect to the other (as referenced in an X-Zplane) at an angle ranging from 10° to 180°, such as from 15° to 170°,such as from 20° to 160°, such as from 25° to 150°, such as from 30° to120° and including from 45° to 90°. The photodetector array may be anysuitable shape and may be a rectilinear shape, e.g., squares,rectangles, trapezoids, triangles, hexagons, etc., curvilinear shapes,e.g., circles, ovals; as well as irregular shapes, e.g., a parabolicbottom portion coupled to a planar top portion. In certain embodiments,the photodetector array has a rectangular-shaped active surface.

Each photodetector (e.g., photodiode) in the array may have an activesurface with a width that ranges from 5 μm to 250 μm, such as from 10 μmto 225 μm, such as from 15 μm to 200 μm, such as from 20 μm to 175 μm,such as from 25 μm to 150 μm, such as from 30 μm to 125 μm and includingfrom 50 μm to 100 μm and a length that ranges from 5 μm to 250 μm, suchas from 10 μm to 225 μm, such as from 15 μm to 200 μm, such as from 20μm to 175 μm, such as from 25 μm to 150 μm, such as from 30 μm to 125 μmand including from 50 μm to 100 μm, where the surface area of eachphotodetector (e.g., photodiode) in the array ranges from 25 to μm² to10000 μm², such as from 50 to μm² to 9000 μm², such as from 75 to μm² to8000 μm², such as from 100 to μm² to 7000 μm², such as from 150 to μm²to 6000 μm² and including from 200 to μm² to 5000 μm².

The size of the photodetector array may vary depending on the amount andintensity of the light, the number of photodetectors and the desiredsensitivity and may have a length that ranges from 0.01 mm to 100 mm,such as from 0.05 mm to 90 mm, such as from 0.1 mm to 80 mm, such asfrom 0.5 mm to 70 mm, such as from 1 mm to 60 mm, such as from 2 mm to50 mm, such as from 3 mm to 40 mm, such as from 4 mm to 30 mm andincluding from 5 mm to 25 mm. The width of the photodetector array mayalso vary, ranging from 0.01 mm to 100 mm, such as from 0.05 mm to 90mm, such as from 0.1 mm to 80 mm, such as from 0.5 mm to 70 mm, such asfrom 1 mm to 60 mm, such as from 2 mm to 50 mm, such as from 3 mm to 40mm, such as from 4 mm to 30 mm and including from 5 mm to 25 mm. Assuch, the active surface of the photodetector array may range from 0.1mm² to 10000 mm², such as from 0.5 mm² to 5000 mm², such as from 1 mm²to 1000 mm², such as from 5 mm² to 500 mm², and including from 10 mm² to100 mm².

Photodetectors of interest are configured to measure collected light atone or more wavelengths, such as at 2 or more wavelengths, such as at 5or more different wavelengths, such as at 10 or more differentwavelengths, such as at 25 or more different wavelengths, such as at 50or more different wavelengths, such as at 100 or more differentwavelengths, such as at 200 or more different wavelengths, such as at300 or more different wavelengths and including measuring light emittedby a sample in the flow stream at 400 or more different wavelengths.

In some embodiments, photodetectors are configured to measure collectedlight over a range of wavelengths (e.g., 200 nm-1000 nm). In certainembodiments, photodetectors of interest are configured to collectspectra of light over a range of wavelengths. For example, systems mayinclude one or more detectors configured to collect spectra of lightover one or more of the wavelength ranges of 200 nm-1000 nm. In yetother embodiments, detectors of interest are configured to measure lightfrom the sample in the flow stream at one or more specific wavelengths.For example, systems may include one or more detectors configured tomeasure light at one or more of 450 nm, 518 nm, 519 nm, 561 nm, 578 nm,605 nm, 607 nm, 625 nm, 650 nm, 660 nm, 667 nm; 670 nm, 668 nm, 695 nm,710 nm; 723 nm, 780 nm, 785 nm, 647 nm, 617 nm and any combinationsthereof.

The light detection system is configured to measure light continuouslyor in discrete intervals. In some instances, photodetectors of interestare configured to take measurements of the collected light continuously.In other instances, the light detection system is configured to takemeasurements in discrete intervals, such as measuring light every 0.001millisecond, every 0.01 millisecond, every 0.1 millisecond, every 1millisecond, every 10 milliseconds, every 100 milliseconds and includingevery 1000 milliseconds, or some other interval.

In embodiments, systems are configured to analyze light from theirradiated sample and to generate spatial data of the object in the flowstream. Systems of interest may include computer controlled systemswhere the systems further include one or more computers for completeautomation or partial automation of a system for practicing methodsdescribed herein. In some embodiments, systems include a computer havinga computer readable storage medium with a computer program storedthereon, where the computer program when loaded on the computer includesinstructions for irradiating a flow cell having a sample in a flowstream with a light source and detecting light from the flow cell with alight detection system having a plurality of photodetectors andgenerating spatial data of the object in the flow stream and determiningwhether the object is an aggregate (e.g., cell aggregate) based on thespatial data.

In some embodiments, systems are configured to generate an image of anobject in the flow stream in real time, such as for example so that oneor more components of the sample can be sorted based on the generatedimage. In some embodiments, systems include a computer having a computerreadable storage medium with a computer program stored thereon, wherethe computer program when loaded on the computer includes instructionsfor irradiating a flow cell having a sample in a flow stream with alight source and detecting light from the flow cell with a lightdetection system having a plurality of photodetectors and generating animage of an object in the flow stream and determining whether the imagedobject is an aggregate (e.g., cell aggregate) based on the generatedimage.

In some embodiments, systems include a computer having a computerreadable storage medium with a computer program stored thereon, wherethe computer program when loaded on the computer further includesinstructions for generating an image of an object in the flow streamfrom the detected light. The image may be generated from detected lightabsorption, detected light scatter, detected light emission or anycombination thereof. In certain embodiments, the image of the object inthe flow stream is a greyscale image. In some instances, the greyscaleimage is generated by the subject system from light absorption detectedfrom the sample, such as from a brightfield light detector. In theseinstances, the greyscale image is generated based on brightfield imagedata from the cell in the flow stream. In other instances, the greyscaleimage is generated by the subject system from light scatter detectedfrom the sample, such as from a side scatter detector, a forward scatterdetector or a combination of a side scatter detector and forward scatterdetector. In these instances, the greyscale image is generated based onscattered light image data. In yet other instances, the greyscale imageis generated by the subject system from emitted light from the sample,such as light from fluorophores added to the sample. In these instances,the greyscale image is generated based on fluorescent image data (i.e.,imaging data from fluorescent compounds on or in the cell). In stillother instances, the greyscale image is generated by the subject systemfrom a combination of detected light absorption, detected light scatterand detected light emission.

The subject systems may be configured to generate one or more images ofthe object from the detected light. In some embodiments, a single imageis generated from each form of detected light. For example, a firstimage of the object is generated from detected light absorption; asecond image of the object is generated from detected light scatter anda third image of the object is generated from detected light emission.In other embodiments, two or more images are generated from each form ofdetected light, such as 3 or more, such as 4 or more, such as 5 or moreand including 10 or more images or a combination thereof.

In some embodiments, systems include a computer having a computerreadable storage medium with a computer program stored thereon, wherethe computer program when loaded on the computer further includesinstructions for generating an image mask of the object. In theseembodiments, systems include memory with instructions for determining apixel intensity threshold value from the greyscale image. In someembodiments, the computer program includes instructions which whenexecuted by the processor cause the processor to determine the pixelintensity threshold value from the greyscale image by minimizing theintra-class variance of the greyscale image and calculating a pixelintensity threshold that is based on the minimized intra-class variance(or where inter-class variance is maximal).

Systems include memory with instructions to compare each pixel in thegreyscale image against the determined intensity threshold value and toconvert each pixel to a binary pixel value. In some embodiments, thememory includes instructions to compare pixels along each horizontal rowin the greyscale image against the determined intensity threshold value.In some instances, the memory includes instructions to compare pixelsagainst the determined intensity threshold value from the left side ofthe greyscale image to the right side of the greyscale image. In otherinstances, the memory includes instructions to compare pixels againstthe determined intensity threshold value from the right side of thegreyscale image to the left side of the greyscale image. In otherembodiments, the memory includes instructions to compare pixels alongeach vertical column in the greyscale image against the determinedintensity threshold value. In some instances, the memory includesinstructions to compare pixels against the determined intensitythreshold value from the top of the greyscale image to the bottom of thegreyscale image along each vertical column. In other instances, thememory includes instructions to compare pixels against the determinedintensity threshold value from the bottom of the greyscale image to thetop of the greyscale image along each vertical column.

Depending on the type of light detected, each pixel is assigned a binarypixel value of 1 or a binary pixel value of 0. In one example, systemsinclude a computer program that includes instructions for detectinglight absorption (e.g., brightfield image data) from the flow stream andassigning a binary pixel value of 1 to each pixel in the greyscale imagewhen the pixel intensity is less than the intensity threshold value andassigning a binary pixel value of 0 when the pixel intensity of thegreyscale image is greater than the intensity threshold value. Inanother example, systems include a computer program that includesinstructions for detecting light scatter from the object in the flowstream and assigning a binary pixel value of 1 to each pixel in thegreyscale image when the pixel intensity is greater than the intensitythreshold value and assigning a binary pixel value of 0 when the pixelintensity is less than the intensity threshold value. In yet anotherexample, systems include a computer program that includes instructionsfor detecting fluorescence from the object in the flow stream andassigning a binary pixel value of 1 to each pixel in the greyscale imagewhen the pixel intensity is greater than the intensity threshold valueand assigning a binary pixel value of 0 when the pixel intensity is lessthan the intensity threshold value.

Where a binary pixel value is assigned by the subject system to eachpixel in the greyscale image across a horizontal row, in someembodiments systems include a computer program that includesinstructions for further determining the first pixel across thehorizontal row having a binary pixel value of 1 and determining the lastpixel in the horizontal row having a binary pixel value of 1. In oneexample, systems include a computer program that includes instructionsfor determining the first pixel from the left side of the horizontal rowhaving an assigned binary pixel value of 1 and determining the lastpixel from the left side of horizontal row having an assigned binarypixel value of 1. In another example, systems include a computer programthat includes instructions for determining the first pixel from theright side of the horizontal row having an assigned binary pixel valueof 1 and determining the last pixel from the right side of horizontalrow having an assigned binary pixel value of 1. In other embodiments,systems include a computer program that includes instructions forfurther determining the first pixel across the horizontal row having abinary pixel value of 0 and determining the last pixel in the horizontalrow having a binary pixel value of 0. In one example, systems include acomputer program that includes instructions for determining the firstpixel from the left side of the horizontal row having an assigned binarypixel value of 0 and determining the last pixel from the left side ofhorizontal row having an assigned binary pixel value of 0. In anotherexample, systems include a computer program that includes instructionsfor determining the first pixel from the right side of the horizontalrow having an assigned binary pixel value of 0 and determining the lastpixel from the right side of horizontal row having an assigned binarypixel value of 0.

Where a binary pixel value is assigned to each pixel in the greyscaleimage along a vertical column, in some embodiments systems include acomputer program that includes instructions for further determining thefirst pixel along the vertical column having a binary pixel value of 1and determining the last pixel along the vertical column having a binarypixel value of 1. In one example, systems include a computer programthat includes instructions for determining the first pixel from the topof the vertical column having an assigned binary pixel value of 1 anddetermining the last pixel from the top of the vertical column having anassigned binary pixel value of 1. In another example, systems include acomputer program that includes instructions for determining the firstpixel from the bottom of the vertical column having an assigned binarypixel value of 1 and determining the last pixel from the bottom of thevertical column having an assigned binary pixel value of 1. In otherembodiments, systems include a computer program that includesinstructions for further determining the first pixel along a verticalcolumn having a binary pixel value of 0 and determining the last pixelin the vertical column having a binary pixel value of 0. In one example,systems include a computer program that includes instructions fordetermining the first pixel from the top of the vertical column havingan assigned binary pixel value of 0 and determining the last pixel fromthe top of the vertical column having an assigned binary pixel value of0. In another example, systems include a computer program that includesinstructions for determining the first pixel from the bottom of thevertical column having an assigned binary pixel value of 0 anddetermining the last pixel from bottom of the vertical column having anassigned binary pixel value of 0.

In some embodiments, systems include a computer having a computerreadable storage medium with a computer program stored thereon, wherethe computer program when loaded on the computer further includesinstructions for determining one or more properties of the object basedon the calculated image moment and spatial data. For example, systemsmay include memory having instructions for determining the size of thecell, the center of mass of the cell or the eccentricity of the cellbased on the calculated image moment and spatial data. In theseembodiments, systems are configured to calculate one or more imagemoments and then determine the characteristic of the cell using both thecalculated image moment and spatial data.

In some embodiments, systems include a computer having a computerreadable storage medium with a computer program stored thereon, wherethe computer program when loaded on the computer further includesinstructions for determining one or more properties of the object basedon the calculated image moment and generated image. For example, systemsmay include memory having instructions for determining the size of thecell, the center of mass of the cell or the eccentricity of the cellbased on the calculated image moment and generated image of the object(e.g., image or image mask). In these embodiments, systems areconfigured to calculate one or more image moments and then determine thecharacteristic of the cell using both the calculated image moment andthe image of the object.

In some instances, the center of mass may be calculated from the imagemoment and the generated image of the object. For example, systems maybe configured to determine the center of mass of the object from thecalculated image moment and the generated image according to:

${{Center}\mspace{14mu}{of}\mspace{14mu}{Mass}} = \frac{M_{1,0}}{M_{0,0}}$

In other instances, the orientation of the object may be calculated fromthe image moment and the generated image of the object. For example,systems may be configured to determine the orientation of the objectfrom the calculated image moment and the generated image according to:

${{Orientation}\mspace{14mu}{of}\mspace{14mu}{cell}} = {\frac{1}{2}\arctan\frac{2\left( {M_{1,1} - \frac{M_{1,0}M_{0,1}}{M_{0,0}}} \right)}{M_{2,0} - \frac{M_{2,0}^{2}}{M_{0,0}} - M_{0,2} + \frac{M_{0,2}^{2}}{M_{0,0}}}}$

In still other instances, the eccentricity of the object may becalculated from the image moment and the generated image of the object.For example, systems may be configured to determine the eccentricity ofthe object from the calculated image moment and the generated imageaccording to:

${Eccentricity} = \frac{\begin{matrix}{M_{2,0} - \frac{M_{2,0}^{2}}{M_{0,0}^{2}} + M_{0,2} - \frac{M_{0,2}^{2}}{M_{0,0}^{2}} -} \\\sqrt{{4\left( {M_{1,1} - \frac{M_{1,0}M_{0,1}}{M_{0,0}^{2}}} \right)^{2}} + \left( {M_{2,0} - \frac{M_{2,0}^{2}}{M_{0,0}} - M_{0,2} + \frac{M_{0,2}^{2}}{M_{0,0}}} \right)^{2}}\end{matrix}}{\begin{matrix}{M_{2,0} - \frac{M_{2,0}^{2}}{M_{0,0}^{2}} + M_{0,2} - \frac{M_{0,2}^{2}}{M_{0,0}^{2}} +} \\\sqrt{{4\left( {M_{1,1} - \frac{M_{1,0}M_{0,1}}{M_{0,0}^{2}}} \right)^{2}} + \left( {M_{2,0} - \frac{M_{2,0}^{2}}{M_{0,0}} - M_{0,2} + \frac{M_{0,2}^{2}}{M_{0,0}}} \right)^{2}}\end{matrix}}$

In certain embodiments, systems include a computer having a computerreadable storage medium with a computer program stored thereon, wherethe computer program when loaded on the computer further includesinstructions for assessing one or more of: 1) the calculated size of theobject; 2) the center of mass of the object; and 3) the eccentricity ofthe object and identifying whether the object is an aggregate ofparticles (e.g., a cell aggregate) or a single particle (e.g., a singlecell). In some instances, systems are configured to determine that theobject is an aggregate based on the calculated size of the object. Inother instances, systems are configured to determine that the object isan aggregate based on the calculated center of mass of the object. Inyet other instances, systems are configured to determine that the objectis an aggregate based on the calculated eccentricity of the object.

In certain instances, systems include memory having instructions for: 1)assessing one or more properties of the object based on the calculatedimage moment and spatial data; and 2) assessing a light scatter detectoroutput signal from the object in the interrogation region of the flowstream. In these embodiments, light scatter detector output signal maybe a forward light scatter detector output or a side light scatterdetector output or a combination thereof. In embodiments, the lightscatter detector output signal may be a signal pulse width, a signalpulse height and a signal pulse area or a combination thereof. In oneexample, systems include memory having instructions for assessing one ormore properties of the object (size, center of mass, eccentricity) basedon the calculated image moment and spatial data and assessing the pulsewidth of a light scatter detector output signal. In another example,systems include memory having instructions for assessing one or moreproperties of the object (size, center of mass, eccentricity) based onthe calculated image moment and spatial data and assessing the pulseheight of a light scatter detector output signal. In yet anotherexample, systems include memory having instructions for assessing one ormore properties of the object (size, center of mass, eccentricity) basedon the calculated image moment and spatial data and assessing the pulsearea of a light scatter detector output signal.

In certain embodiments, systems include memory having instructionsfor: 1) assessing each of the pulse width, the pulse height and pulsearea of a light scatter detector output signal, followed by 2) assessingone or more properties of the object (size, center of mass,eccentricity) based on the calculated image moment and spatial data. Inthese embodiments, the light scatter detector output signals may becollected from one or more of a side scatter detector and a forwardscatter detector. In some instances, light scatter detector outputsignals used by the subject systems according to these embodiments arecollected from a side scatter detector. In other instances, lightscatter detector output signals used are collected from a forwardscatter detector. In yet other instances, light scatter detector outputsignals used are collected from both a side scatter detector and aforward scatter detector.

In certain instances, systems include memory having instructions for: 1)assessing one or more properties of the object based on the calculatedimage moment and generated image; and 2) assessing a light scatterdetector output signal from the object in the interrogation region ofthe flow stream. In these embodiments, light scatter detector outputsignal may be a forward light scatter detector output or a side lightscatter detector output or a combination thereof. In embodiments, thelight scatter detector output signal may be a signal pulse width, asignal pulse height and a signal pulse area or a combination thereof. Inone example, systems include memory having instructions for assessingone or more properties of the object (size, center of mass,eccentricity) based on the calculated image moment and generated imageand assessing the pulse width of a light scatter detector output signal.In another example, systems include memory having instructions forassessing one or more properties of the object (size, center of mass,eccentricity) based on the calculated image moment and generated imageand assessing the pulse height of a light scatter detector outputsignal. In yet another example, systems include memory havinginstructions for assessing one or more properties of the object (size,center of mass, eccentricity) based on the calculated image moment andgenerated image and assessing the pulse area of a light scatter detectoroutput signal.

In certain embodiments, systems include memory having instructionsfor: 1) assessing each of the pulse width, the pulse height and pulsearea of a light scatter detector output signal, followed by 2) assessingone or more properties of the object (size, center of mass,eccentricity) based on the calculated image moment and generated image.In these embodiments, the light scatter detector output signals may becollected from one or more of a side scatter detector and a forwardscatter detector. In some instances, light scatter detector outputsignals used by the subject systems according to these embodiments arecollected from a side scatter detector. In other instances, lightscatter detector output signals used are collected from a forwardscatter detector. In yet other instances, light scatter detector outputsignals used are collected from both a side scatter detector and aforward scatter detector.

In some instances, systems include memory having instructions fordetermining that the object is a horizontal cell aggregate where two ormore cells are aligned together across a horizontal axis of the flowstream, such as 3 or more cells, such as 4 or more cells and including 5or more cells. In other instances, systems include memory havinginstructions for determining that the object is a vertical cellaggregate where two or more cells are aligned together along a verticalaxis (i.e., longitudinal axis) of the flow stream, such as 3 or morecells, such as 4 or more cells and including 5 or more cells. In yetother instances, systems include memory having instructions fordetermining that the object is combination cell aggregate having two ormore cells aligned together across a horizontal axis and having two ormore cells aligned together along a vertical axis.

In some embodiments, systems include memory having instructions tocalculate the spatial data from the frequency-encoded data of the objectin the flow stream. In these embodiments, systems are configured tocalculate the spatial data by performing a transform offrequency-encoded data. In one example, the spatial data is calculatedby performing a Fourier transform (FT) of the frequency-encoded data. Inanother example, the spatial data is calculated by performing a discreteFourier transform (DFT) of the frequency-encoded data. In yet anotherexample, the spatial data is calculated by performing a short timeFourier transform (STET) of the frequency-encoded data. In still anotherexample, the spatial data is calculated with a digital lock-in amplifierto heterodyne and de-multiplex the frequency-encoded data.

Sorting systems according to some embodiments, may include a display andoperator input device. Operator input devices may, for example, be akeyboard, mouse, or the like. The processing module includes a processorwhich has access to a memory having instructions stored thereon forperforming the steps of the subject methods. The processing module mayinclude an operating system, a graphical user interface (GUI)controller, a system memory, memory storage devices, and input-outputcontrollers, cache memory, a data backup unit, and many other devices.The processor may be a commercially available processor or it may be oneof other processors that are or will become available. The processorexecutes the operating system and the operating system interfaces withfirmware and hardware in a well-known manner, and facilitates theprocessor in coordinating and executing the functions of variouscomputer programs that may be written in a variety of programminglanguages, such as Java, Perl, C++, other high level or low-levellanguages, as well as combinations thereof, as is known in the art. Theoperating system, typically in cooperation with the processor,coordinates and executes functions of the other components of thecomputer. The operating system also provides scheduling, input-outputcontrol, file and data management, memory management, and communicationcontrol and related services, all in accordance with known techniques.The processor may be any suitable analog or digital system. In someembodiments, the processor includes analog electronics which providefeedback control, such as for example negative feedback control.

The system memory may be any of a variety of known or future memorystorage devices. Examples include any commonly available random-accessmemory (RAM), magnetic medium such as a resident hard disk or tape, anoptical medium such as a read and write compact disc, flash memorydevices, or other memory storage device. The memory storage device maybe any of a variety of known or future devices, including a compact diskdrive, a tape drive, a removable hard disk drive, or a diskette drive.Such types of memory storage devices typically read from, and/or writeto, a program storage medium (not shown) such as, respectively, acompact disk, magnetic tape, removable hard disk, or floppy diskette.Any of these program storage media, or others now in use or that maylater be developed, may be considered a computer program product. Aswill be appreciated, these program storage media typically store acomputer software program and/or data. Computer software programs, alsocalled computer control logic, typically are stored in system memoryand/or the program storage device used in conjunction with the memorystorage device.

In some embodiments, a computer program product is described comprisinga computer usable medium having control logic (computer softwareprogram, including program code) stored therein. The control logic, whenexecuted by the processor the computer, causes the processor to performfunctions described herein. In other embodiments, some functions areimplemented primarily in hardware using, for example, a hardware statemachine. Implementation of the hardware state machine so as to performthe functions described herein will be apparent to those skilled in therelevant arts.

Memory may be any suitable device in which the processor can store andretrieve data, such as magnetic, optical, or solid-state storage devices(including magnetic or optical disks or tape or RAM, or any othersuitable device, either fixed or portable). The processor may include ageneral-purpose digital microprocessor suitably programmed from acomputer readable medium carrying necessary program code. Programmingcan be provided remotely to processor through a communication channel,or previously saved in a computer program product such as memory or someother portable or fixed computer readable storage medium using any ofthose devices in connection with memory. For example, a magnetic oroptical disk may carry the programming, and can be read by a diskwriter/reader. Systems of the invention also include programming, e.g.,in the form of computer program products, algorithms for use inpracticing the methods as described above. Programming according to thepresent invention can be recorded on computer readable media, e.g., anymedium that can be read and accessed directly by a computer. Such mediainclude, but are not limited to: magnetic storage media, such as floppydiscs, hard disc storage medium, and magnetic tape; optical storagemedia such as CD-ROM; electrical storage media such as RAM and ROM;portable flash drive; and hybrids of these categories such asmagnetic/optical storage media.

The processor may also have access to a communication channel tocommunicate with a user at a remote location. By remote location ismeant the user is not directly in contact with the system and relaysinput information to an input manager from an external device, such as acomputer connected to a Wide Area Network (“WAN”), telephone network,satellite network, or any other suitable communication channel,including a mobile telephone (i.e., smartphone).

In some embodiments, systems according to the present disclosure may beconfigured to include a communication interface. In some embodiments,the communication interface includes a receiver and/or transmitter forcommunicating with a network and/or another device. The communicationinterface can be configured for wired or wireless communication,including, but not limited to, radio frequency (RF) communication (e.g.,Radio-Frequency Identification (RFID), Zigbee communication protocols,WiFi, infrared, wireless Universal Serial Bus (USB), Ultra-Wide Band(UWB), Bluetooth® communication protocols, and cellular communication,such as code division multiple access (CDMA) or Global System for Mobilecommunications (GSM).

In one embodiment, the communication interface is configured to includeone or more communication ports, e.g., physical ports or interfaces suchas a USB port, an RS-232 port, or any other suitable electricalconnection port to allow data communication between the subject systemsand other external devices such as a computer terminal (for example, ata physician's office or in hospital environment) that is configured forsimilar complementary data communication.

In one embodiment, the communication interface is configured forinfrared communication, Bluetooth® communication, or any other suitablewireless communication protocol to enable the subject systems tocommunicate with other devices such as computer terminals and/ornetworks, communication enabled mobile telephones, personal digitalassistants, or any other communication devices which the user may use inconjunction.

In one embodiment, the communication interface is configured to providea connection for data transfer utilizing Internet Protocol (IP) througha cell phone network, Short Message Service (SMS), wireless connectionto a personal computer (PC) on a Local Area Network (LAN) which isconnected to the internet, or WiFi connection to the internet at a WiFihotspot.

In one embodiment, the subject systems are configured to wirelesslycommunicate with a server device via the communication interface, e.g.,using a common standard such as 802.11 or Bluetooth® RF protocol, or anIrDA infrared protocol. The server device may be another portabledevice, such as a smart phone, Personal Digital Assistant (PDA) ornotebook computer; or a larger device such as a desktop computer,appliance, etc. In some embodiments, the server device has a display,such as a liquid crystal display (LCD), as well as an input device, suchas buttons, a keyboard, mouse or touch-screen.

In some embodiments, the communication interface is configured toautomatically or semi-automatically communicate data stored in thesubject systems, e.g., in an optional data storage unit, with a networkor server device using one or more of the communication protocols and/ormechanisms described above.

Output controllers may include controllers for any of a variety of knowndisplay devices for presenting information to a user, whether a human ora machine, whether local or remote. If one of the display devicesprovides visual information, this information typically may be logicallyand/or physically organized as an array of picture elements. A graphicaluser interface (GUI) controller may include any of a variety of known orfuture software programs for providing graphical input and outputinterfaces between the system and a user, and for processing userinputs. The functional elements of the computer may communicate witheach other via system bus. Some of these communications may beaccomplished in alternative embodiments using network or other types ofremote communications. The output manager may also provide informationgenerated by the processing module to a user at a remote location, e.g.,over the Internet, phone or satellite network, in accordance with knowntechniques. The presentation of data by the output manager may beimplemented in accordance with a variety of known techniques. As someexamples, data may include SQL, HTML or XML documents, email or otherfiles, or data in other forms. The data may include Internet URLaddresses so that a user may retrieve additional SQL, HTML, XML, orother documents or data from remote sources. The one or more platformspresent in the subject systems may be any type of known computerplatform or a type to be developed in the future, although theytypically will be of a class of computer commonly referred to asservers. However, they may also be a main-frame computer, a workstation, or other computer type. They may be connected via any known orfuture type of cabling or other communication system including wirelesssystems, either networked or otherwise. They may be co-located or theymay be physically separated. Various operating systems may be employedon any of the computer platforms, possibly depending on the type and/ormake of computer platform chosen. Appropriate operating systems includeWindows 10, Windows NT®, Windows XP, Windows 7, Windows 8, iOS, SunSolaris, Linux, OS/400, Compaq Tru64 Unix, SGI IRIX, Siemens ReliantUnix, Ubuntu, Zorin OS and others.

In certain embodiments, the subject systems include one or more opticaladjustment components for adjusting the light such as light irradiatedonto the sample (e.g., from a laser) or light collected from the sample(e.g., scattered, fluorescence). For example, the optical adjustment maybe to increase the dimensions of the light, the focus of the light or tocollimate the light. In some instances, optical adjustment is amagnification protocol so as to increase the dimensions of the light(e.g., beam spot), such as increasing the dimensions by 5% or more, suchas by 10% or more, such as by 25% or more, such as by 50% or more andincluding increasing the dimensions by 75% or more. In otherembodiments, optical adjustment includes focusing the light so as toreduce the light dimensions, such as by 5% or greater, such as by 10% orgreater, such as by 25% or greater, such as by 50% or greater andincluding reducing the dimensions of the beam spot by 75% or greater. Incertain embodiments, optical adjustment includes collimating the light.The term “collimate” is used in its conventional sense to refer to theoptically adjusting the collinearity of light propagation or reducingdivergence by the light of from a common axis of propagation. In someinstances, collimating includes narrowing the spatial cross section of alight beam (e.g., reducing the beam profile of a laser)

In some embodiments, the optical adjustment component is a focusing lenshaving a magnification ratio of from 0.1 to 0.95, such as amagnification ratio of from 0.2 to 0.9, such as a magnification ratio offrom 0.3 to 0.85, such as a magnification ratio of from 0.35 to 0.8,such as a magnification ratio of from 0.5 to 0.75 and including amagnification ratio of from 0.55 to 0.7, for example a magnificationratio of 0.6. For example, the focusing lens is, in certain instances, adouble achromatic de-magnifying lens having a magnification ratio ofabout 0.6. The focal length of the focusing lens may vary, ranging from5 mm to 20 mm, such as from 6 mm to 19 mm, such as from 7 mm to 18 mm,such as from 8 mm to 17 mm, such as from 9 mm to 16 and including afocal length ranging from 10 mm to 15 mm. In certain embodiments, thefocusing lens has a focal length of about 13 mm.

In other embodiments, the optical adjustment component is a collimator.The collimator may be any convenient collimating protocol, such as oneor more mirrors or curved lenses or a combination thereof. For example,the collimator is in certain instances a single collimating lens. Inother instances, the collimator is a collimating mirror. In yet otherinstances, the collimator includes two lenses. In still other instances,the collimator includes a mirror and a lens. Where the collimatorincludes one or more lenses, the focal length of the collimating lensmay vary, ranging from 5 mm to 40 mm, such as from 6 mm to 37.5 mm, suchas from 7 mm to 35 mm, such as from 8 mm to 32.5 mm, such as from 9 mmto 30 mm, such as from 10 mm to 27.5 mm, such as from 12.5 mm to 25 mmand including a focal length ranging from 15 mm to 20 mm.

In some embodiments, the subject systems include a flow cell nozzlehaving a nozzle orifice configured to flow a flow stream through theflow cell nozzle. The subject flow cell nozzle has an orifice whichpropagates a fluidic sample to a sample interrogation region, where insome embodiments, the flow cell nozzle includes a proximal cylindricalportion defining a longitudinal axis and a distal frustoconical portionwhich terminates in a flat surface having the nozzle orifice that istransverse to the longitudinal axis. The length of the proximalcylindrical portion (as measured along the longitudinal axis) may varyranging from 1 mm to 15 mm, such as from 1.5 mm to 12.5 mm, such as from2 mm to 10 mm, such as from 3 mm to 9 mm and including from 4 mm to 8mm. The length of the distal frustoconical portion (as measured alongthe longitudinal axis) may also vary, ranging from 1 mm to 10 mm, suchas from 2 mm to 9 mm, such as from 3 mm to 8 mm and including from 4 mmto 7 mm. The diameter of the of the flow cell nozzle chamber may vary,in some embodiments, ranging from 1 mm to 10 mm, such as from 2 mm to 9mm, such as from 3 mm to 8 mm and including from 4 mm to 7 mm.

In certain instances, the nozzle chamber does not include a cylindricalportion and the entire flow cell nozzle chamber is frustoconicallyshaped. In these embodiments, the length of the frustoconical nozzlechamber (as measured along the longitudinal axis transverse to thenozzle orifice), may range from 1 mm to 15 mm, such as from 1.5 mm to12.5 mm, such as from 2 mm to 10 mm, such as from 3 mm to 9 mm andincluding from 4 mm to 8 mm. The diameter of the proximal portion of thefrustoconical nozzle chamber may range from 1 mm to 10 mm, such as from2 mm to 9 mm, such as from 3 mm to 8 mm and including from 4 mm to 7 mm.

In embodiments, the sample flow stream emanates from an orifice at thedistal end of the flow cell nozzle. Depending on the desiredcharacteristics of the flow stream, the flow cell nozzle orifice may beany suitable shape where cross-sectional shapes of interest include, butare not limited to: rectilinear cross sectional shapes, e.g., squares,rectangles, trapezoids, triangles, hexagons, etc., curvilinearcross-sectional shapes, e.g., circles, ovals, as well as irregularshapes, e.g., a parabolic bottom portion coupled to a planar topportion. In certain embodiments, flow cell nozzle of interest has acircular orifice. The size of the nozzle orifice may vary, in someembodiments ranging from 1 μm to 20000 μm, such as from 2 μm to 17500μm, such as from 5 μm to 15000 μm, such as from 10 μm to 12500 μm, suchas from 15 μm to 10000 μm, such as from 25 μm to 7500 μm, such as from50 μm to 5000 μm, such as from 75 μm to 1000 μm, such as from 100 μm to750 μm and including from 150 μm to 500 μm. In certain embodiments, thenozzle orifice is 100 μm.

In some embodiments, the flow cell nozzle includes a sample injectionport configured to provide a sample to the flow cell nozzle. Inembodiments, the sample injection system is configured to providesuitable flow of sample to the flow cell nozzle chamber. Depending onthe desired characteristics of the flow stream, the rate of sampleconveyed to the flow cell nozzle chamber by the sample injection portmay be 1 μL/sec or more, such as 2 μL/sec or more, such as 3 μL/sec ormore, such as 5 μL/sec or more, such as 10 μL/sec or more, such as 15μL/sec or more, such as 25 μL/sec or more, such as 50 μL/sec or more,such as 100 μL/sec or more, such as 150 μL/sec or more such as 200μL/sec or more, such as 250 μL/sec or more, such as 300 μL/sec or more,such as 350 μL/sec or more, such as 400 μL/sec or more, such as 450μL/sec or more and including 500 μL/sec or more. For example, the sampleflow rate may range from 1 μL/sec to about 500 μL/sec, such as from 2μL/sec to about 450 μL/sec, such as from 3 μL/sec to about 400 μL/sec,such as from 4 μL/sec to about 350 μL/sec, such as from 5 μL/sec toabout 300 μL/sec, such as from 6 μL/sec to about 250 μL/sec, such asfrom 7 μL/sec to about 200 μL/sec, such as from 8 μL/sec to about 150μL/sec, such as from 9 μL/sec to about 125 μL/sec and including from 10μL/sec to about 100 μL/sec.

The sample injection port may be an orifice positioned in a wall of thenozzle chamber or may be a conduit positioned at the proximal end of thenozzle chamber. Where the sample injection port is an orifice positionedin a wall of the nozzle chamber, the sample injection port orifice maybe any suitable shape where cross-sectional shapes of interest include,but are not limited to: rectilinear cross sectional shapes, e.g.,squares, rectangles, trapezoids, triangles, hexagons, etc., curvilinearcross-sectional shapes, e.g., circles, ovals, etc., as well as irregularshapes, e.g., a parabolic bottom portion coupled to a planar topportion. In certain embodiments, the sample injection port has acircular orifice. The size of the sample injection port orifice may varydepending on shape, in certain instances, having an opening ranging from0.1 mm to 5.0 mm, e.g., 0.2 to 3.0 mm, e.g., 0.5 mm to 2.5 mm, such asfrom 0.75 mm to 2.25 mm, such as from 1 mm to 2 mm and including from1.25 mm to 1.75 mm, for example 1.5 mm.

In certain instances, the sample injection port is a conduit positionedat a proximal end of the flow cell nozzle chamber. For example, thesample injection port may be a conduit positioned to have the orifice ofthe sample injection port in line with the flow cell nozzle orifice.Where the sample injection port is a conduit positioned in line with theflow cell nozzle orifice, the cross-sectional shape of the sampleinjection tube may be any suitable shape where cross-sectional shapes ofinterest include, but are not limited to: rectilinear cross sectionalshapes, e.g., squares, rectangles, trapezoids, triangles, hexagons,etc., curvilinear cross-sectional shapes, e.g., circles, ovals, as wellas irregular shapes, e.g., a parabolic bottom portion coupled to aplanar top portion. The orifice of the conduit may vary depending onshape, in certain instances, having an opening ranging from 0.1 mm to5.0 mm, e.g., 0.2 to 3.0 mm, e.g., 0.5 mm to 2.5 mm, such as from 0.75mm to 2.25 mm, such as from 1 mm to 2 mm and including from 1.25 mm to1.75 mm, for example 1.5 mm. The shape of the tip of the sampleinjection port may be the same or different from the cross-section shapeof the sample injection tube. For example, the orifice of the sampleinjection port may include a beveled tip having a bevel angle rangingfrom 1° to 10°, such as from 2° to 9°, such as from 3° to 8°, such asfrom 4° to 7° and including a bevel angle of 5°.

In some embodiments, the flow cell nozzle also includes a sheath fluidinjection port configured to provide a sheath fluid to the flow cellnozzle. In embodiments, the sheath fluid injection system is configuredto provide a flow of sheath fluid to the flow cell nozzle chamber, forexample in conjunction with the sample to produce a laminated flowstream of sheath fluid surrounding the sample flow stream. Depending onthe desired characteristics of the flow stream, the rate of sheath fluidconveyed to the flow cell nozzle chamber by the may be 25 μL/sec ormore, such as 50 μL/sec or more, such as 75 μL/sec or more, such as 100μL/sec or more, such as 250 μL/sec or more, such as 500 μL/sec or more,such as 750 μL/sec or more, such as 1000 μL/sec or more and including2500 μL/sec or more. For example, the sheath fluid flow rate may rangefrom 1 μL/sec to about 500 μL/sec, such as from 2 μL/sec to about 450μL/sec, such as from 3 μL/sec to about 400 μL/sec, such as from 4 μL/secto about 350 μL/sec, such as from 5 μL/sec to about 300 μL/sec, such asfrom 6 μL/sec to about 250 μL/sec, such as from 7 μL/sec to about 200μL/sec, such as from 8 μL/sec to about 150 μL/sec, such as from 9 μL/secto about 125 μL/sec and including from 10 μL/sec to about 100 μL/sec. Insome embodiments, the sheath fluid injection port is an orificepositioned in a wall of the nozzle chamber. The sheath fluid injectionport orifice may be any suitable shape where cross-sectional shapes ofinterest include, but are not limited to: rectilinear cross sectionalshapes, e.g., squares, rectangles, trapezoids, triangles, hexagons,etc., curvilinear cross-sectional shapes, e.g., circles, ovals, as wellas irregular shapes, e.g., a parabolic bottom portion coupled to aplanar top portion. The size of the sample injection port orifice mayvary depending on shape, in certain instances, having an opening rangingfrom 0.1 mm to 5.0 mm, e.g., 0.2 to 3.0 mm, e.g., 0.5 mm to 2.5 mm, suchas from 0.75 mm to 2.25 mm, such as from 1 mm to 2 mm and including from1.25 mm to 1.75 mm, for example 1.5 mm.

The subject systems, in certain instances, include a sampleinterrogation region in fluid communication with the flow cell nozzleorifice. In these instances, a sample flow stream emanates from anorifice at the distal end of the flow cell nozzle and particles in theflow stream may be irradiated with a light source at the sampleinterrogation region. The size of the interrogation region may varydepending on the properties of the flow nozzle, such as the size of thenozzle orifice and sample injection port size. In embodiments, theinterrogation region may have a width that is 0.01 mm or more, such as0.05 mm or more, such as 0.1 mm or more, such as 0.5 mm or more, such as1 mm or more, such as 2 mm or more, such as 3 mm or more, such as 5 mmor more and including 10 mm or more. The length of the interrogationregion may also vary, ranging in some instances along 0.01 mm or more,such as 0.1 mm or more, such as 0.5 mm or more, such as 1 mm or more,such as 1.5 mm or more, such as 2 mm or more, such as 3 mm or more, suchas 5 mm or more, such as 10 or more, such as 15 mm or more, such as 20mm or more, such as 25 mm or more and including 50 mm or more.

The interrogation region may be configured to facilitate irradiation ofa planar cross-section of an emanating flow stream or may be configuredto facilitate irradiation of a diffuse field (e.g., with a diffuse laseror lamp) of a predetermined length. In some embodiments, theinterrogation region includes a transparent window that facilitatesirradiation of a predetermined length of an emanating flow stream, suchas 1 mm or more, such as 2 mm or more, such as 3 mm or more, such as 4mm or more, such as 5 mm or more and including 10 mm or more. Dependingon the light source used to irradiate the emanating flow stream (asdescribed below), the interrogation region may be configured to passlight that ranges from 100 nm to 1500 nm, such as from 150 nm to 1400nm, such as from 200 nm to 1300 nm; such as from 250 nm to 1200 nm, suchas from 300 nm to 1100 nm, such as from 350 nm to 1000 nm, such as from400 nm to 900 nm and including from 500 nm to 800 nm. As such, theinterrogation region may be formed from any transparent material whichpasses the desired range of wavelength, including but not limited tooptical glass, borosilicate glass, Pyrex glass, ultraviolet quartz,infrared quartz, sapphire as well as plastic, such as polycarbonates,polyvinyl chloride (PVC), polyurethanes, polyethers, polyamides,polyimides, or copolymers of these thermoplastics, such as PETG(glycol-modified polyethylene terephthalate), among other polymericplastic materials, including polyester, where polyesters of interest mayinclude, but are not limited to poly(alkylene terephthalates) such aspoly(ethylene terephthalate) (PET), bottle-grade PET (a copolymer madebased on monoethylene glycol, terephthalic acid, and other comonomerssuch as isophthalic acid, cyclohexene dimethanol, etc.), poly(butyleneterephthalate) (PBT), and poly(hexamethylene terephthalate);poly(alkylene adipates) such as poly(ethylene adipate),poly(1,4-butylene adipate), and poly(hexamethylene adipate);poly(alkylene suberates) such as poly(ethylene suberate); poly(alkylenesebacates) such as poly(ethylene sebacate); poly(ε-caprolactone) andpoly(β-propiolactone); poly(alkylene isophthalates) such aspoly(ethylene isophthalate); poly(alkylene2,6-naphthalene-dicarboxylates) such as poly(ethylene2,6-naphthalene-dicarboxylate); poly(alkylene sulfonyl-4,4′-dibenzoates)such as poly(ethylene sulfonyl-4,4′-dibenzoate); poly(p-phenylenealkylene dicarboxylates) such as poly(p-phenylene ethylenedicarboxylates); poly(trans-1,4-cyclohexanediyl alkylene dicarboxylates)such as poly(trans-1,4-cyclohexanediyl ethylene dicarboxylate);poly(1,4-cyclohexane-dimethylene alkylene dicarboxylates) such aspoly(1,4-cyclohexane-dimethylene ethylene dicarboxylate);poly([2.2.2]-bicyclooctane-1,4-dimethylene alkylene dicarboxylates) suchas poly([2.2.2]-bicyclooctane-1,4-dimethylene ethylene dicarboxylate);lactic acid polymers and copolymers such as (S)-polylactide,(R,S)-polylactide, poly(tetramethylglycolide), andpoly(lactide-co-glycolide); and polycarbonates of bisphenol A,3,3′-dimethylbisphenol A, 3,3′,5,5′-tetrachlorobisphenol A,3,3′,5,5′-tetramethylbisphenol A; polyamides such as poly(p-phenyleneterephthalamide); polyesters, e.g., polyethylene terephthalates, e.g.,Mylar™′ polyethylene terephthalate; etc. In some embodiments, thesubject systems include a cuvette positioned in the sample interrogationregion. In embodiments, the cuvette may pass light that ranges from 100nm to 1500 nm, such as from 150 nm to 1400 nm, such as from 200 nm to1300 nm, such as from 250 nm to 1200 nm, such as from 300 nm to 1100 nm,such as from 350 nm to 1000 nm, such as from 400 nm to 900 nm andincluding from 500 nm to 800 nm.

In some embodiments, the subject systems include a particle sortingcomponent for sorting cells of the sample. In certain instances, theparticle sorting component is a particle sorting module such as thosedescribed in U.S. Patent Publication No. 2017/0299493, filed on Mar. 28,2017 and U.S. Provisional Patent Application No. 62/752,793 filed onOct. 30, 2018, the disclosures of which is incorporated herein byreference. In certain embodiments, the particle sorting componentinclude one or more droplet deflectors such as those described in U.S.Patent Publication No. 2018/0095022, filed on Jun. 14, 2017, thedisclosure of which is incorporated herein by reference.

In some embodiments, the subject systems are flow cytometric systems.Suitable flow cytometry systems may include, but are not limited tothose described in Ormerod (ed.), Flow Cytometry: A Practical Approach,Oxford Univ. Press (1997); Jaroszeski et al. (eds.), Flow CytometryProtocols, Methods in Molecular Biology No. 91, Humana Press (1997);Practical Flow Cytometry, 3rd ed., Wiley-Liss (1995); Virgo, et al.(2012) Ann Clin Biochem. January; 49 (pt 1):17-28; Linden, et. al.,Semin Throm Hemost. 2004 October; 30(5):502-11; Alison, et al. J Pathol,2010 December; 222(4):335-344; and Herbig, et al. (2007) Crit Rev TherDrug Carrier Syst. 24(3):203-255; the disclosures of which areincorporated herein by reference. In certain instances, flow cytometrysystems of interest include BD Biosciences FACSCanto™ II flow cytometer,BD Accuri™ flow cytometer, BD Biosciences FACSCelesta™ flow cytometer,BD Biosciences FACSLyric™ flow cytometer, BD Biosciences FACSVerse™ flowcytometer, BD Biosciences FACSymphony™ flow cytometer BD BiosciencesLSRFortessa™ flow cytometer, BD Biosciences LSRFortess™ X-20 flowcytometer and BD Biosciences FACSCalibur™ cell sorter, a BD BiosciencesFACSCount™ cell sorter, BD Biosciences FACSLyric™ cell sorter and BDBiosciences Via™ cell sorter BD Biosciences Influx™ cell sorter, BDBiosciences Jazz™ cell sorter, BD Biosciences Aria™ cell sorters and BDBiosciences FACSMeIody™ cell sorter, or the like.

In some embodiments, the subject particle sorting systems are flowcytometric systems, such those described in U.S. Pat. Nos. 10,006,852;9,952,076; 9,933,341; 9,784,661; 9,726,527; 9,453,789; 9,200,334;9,097,640; 9,095,494; 9,092,034; 8,975,595; 8,753,573; 8,233,146;8,140,300; 7,544,326; 7,201,875; 7,129,505; 6,821,740; 6,813,017;6,809,804; 6,372,506; 5,700,692; 5,643,796; 5,627,040; 5,620,842;5,602,039; the disclosure of which are herein incorporated by referencein their entirety.

In certain instances, the subject systems are flow cytometry systemsconfigured for imaging particles in a flow stream by fluorescenceimaging using radiofrequency tagged emission (FIRE), such as thosedescribed in Diebold, et al. Nature Photonics Vol. 7(10); 806-810 (2013)as well as described in U.S. Pat. Nos. 9,423,353; 9,784,661 and10,006,852 and U.S. Patent Publication Nos. 2017/0133857 and2017/0350803, the disclosures of which are herein incorporated byreference.

Integrated Circuit Devices

Aspects of the present disclosure also include integrated circuitdevices programmed to generate spatial data of an object in a flowstream and to determine whether the object is an aggregate (e.g., cellaggregate) based on the spatial data. In certain embodiments, thesubject integrated circuit devices are configured to sort the object. Insome embodiments, integrated circuit devices of interest include a fieldprogrammable gate array (FPGA). In other embodiments, integrated circuitdevices include an application specific integrated circuit (ASIC). Inyet other embodiments, integrated circuit devices include a complexprogrammable logic device (CPLD).

Integrated circuit devices according to certain embodiments areprogrammed to generate spatial data of an object in the flow stream. Insome embodiments, the integrated circuit device is programmed togenerate spatial data from data signals from a light absorption detector(e.g., brightfield image data). In other embodiments, the integratedcircuit device is programmed to generate spatial data from data signalsfrom a light scatter detector (e.g., forward scatter image data, sidescatter image data). In yet other embodiments, the integrated circuitdevice is programmed to generate spatial data from data signals from alight emission detector (e.g., fluorescent marker image data). In stillother instances, the integrated circuit device is programmed to generatespatial data of the object from a combination of two or more of detectedlight absorption, detected light scatter and detected fluorescence.

In some embodiments, the integrated circuit device is programmed fordetermining the size of the object based on the spatial data. In otherembodiments, integrated circuit device is programmed for determining thecenter of mass of the object based on the spatial data. In yet otherembodiments, integrated circuit device is programmed for determining theeccentricity of the object based on the spatial data. In certainembodiments, an image moment is calculated based on the spatial data. Insome instances, integrated circuit device is programmed for calculatinga first order image moment of the object along a horizontal axis. Inother instances, integrated circuit device is programmed for calculatinga second order image moment of the object along a horizontal axis. Inyet other instances, integrated circuit device is programmed forcalculating a first order image moment of the object along a verticalaxis. In still other instances, integrated circuit device is programmedfor calculating a second order image moment of the object along avertical axis.

In some embodiments, the integrated circuit device is programmed togenerate an image of the object in the flow stream from detected light.The image may be generated from detected light absorption, detectedlight scatter, detected light emission or any combination thereof. Thesubject integrated circuit devices may be programmed to generate one ormore images of the object from the detected light. In some embodiments,a single image is generated from each from of detected light. Forexample, a first image of the object is generated from detected lightabsorption; a second image of the object is generated from detectedlight scatter and a third image of the object is generated from detectedlight emission. In other embodiments, two or more images are generatedfrom each form of detected light, such as 3 or more, such as 4 or more,such as 5 or more and including 10 or more images or a combinationthereof.

In some embodiments, the integrated circuit device is programmed togenerate an image mask of the object. In these embodiments, theintegrated circuit device is programmed to determine a pixel intensitythreshold value from the greyscale image. In some embodiments, theintegrated circuit device is programmed to determine the pixel intensitythreshold value from the greyscale image by minimizing the intra-classvariance of the greyscale image and calculating a pixel intensitythreshold that is based on the minimized intra-class variance (or whereinter-class variance is maximal).

The integrated circuit device is programmed to compare each pixel in thegreyscale image against the determined intensity threshold value and toconvert each pixel to a binary pixel value. In some embodiments, theintegrated circuit device is programmed to compare pixels along eachhorizontal row in the greyscale image against the determined intensitythreshold value. In some instances, the integrated circuit device isprogrammed to compare pixels against the determined intensity thresholdvalue from the left side of the greyscale image to the right side of thegreyscale image. In other instances, the integrated circuit device isprogrammed to compare pixels against the determined intensity thresholdvalue from the right side of the greyscale image to the left side of thegreyscale image. In other embodiments, the integrated circuit device isprogrammed to compare pixels along each vertical column in the greyscaleimage against the determined intensity threshold value. In someinstances, the integrated circuit device is programmed to compare pixelsagainst the determined intensity threshold value from the top of thegreyscale image to the bottom of the greyscale image along each verticalcolumn. In other instances, the integrated circuit device is programmedto compare pixels against the determined intensity threshold value fromthe bottom of the greyscale image to the top of the greyscale imagealong each vertical column.

Depending on the type of light detected, each pixel is assigned a binarypixel value of 1 or a binary pixel value of 0. In one example, theintegrated circuit device is programmed to detect light absorption(e.g., brightfield image data) from the flow stream and assign a binarypixel value of 1 to each pixel in the greyscale image when the pixelintensity is less than the intensity threshold value and assigning abinary pixel value of 0 when the pixel intensity of the greyscale imageis greater than the intensity threshold value. In another example, theintegrated circuit device is programmed to detect light scatter from theobject in the flow stream and assign a binary pixel value of 1 to eachpixel in the greyscale image when the pixel intensity is greater thanthe intensity threshold value and assigning a binary pixel value of 0when the pixel intensity is less than the intensity threshold value. Inyet another example, the integrated circuit device is programmed todetect fluorescence from the object in the flow stream and assign abinary pixel value of 1 to each pixel in the greyscale image when thepixel intensity is greater than the intensity threshold value andassigning a binary pixel value of 0 when the pixel intensity is lessthan the intensity threshold value.

Where a binary pixel value is assigned by the subject system to eachpixel in the greyscale image across a horizontal row, in someembodiments the integrated circuit device is programmed to determine thefirst pixel across the horizontal row having a binary pixel value of 1and determine the last pixel in the horizontal row having a binary pixelvalue of 1. In one example, the integrated circuit device is programmedto determine the first pixel from the left side of the horizontal rowhaving an assigned binary pixel value of 1 and determine the last pixelfrom the left side of horizontal row having an assigned binary pixelvalue of 1. In another example, the integrated circuit device isprogrammed to determine the first pixel from the right side of thehorizontal row having an assigned binary pixel value of 1 and determinethe last pixel from the right side of horizontal row having an assignedbinary pixel value of 1. In other embodiments, the integrated circuitdevice is programmed to determine the first pixel across the horizontalrow having a binary pixel value of 0 and determine the last pixel in thehorizontal row having a binary pixel value of 0. In one example, theintegrated circuit device is programmed to determine the first pixelfrom the left side of the horizontal row having an assigned binary pixelvalue of 0 and determine the last pixel from the left side of horizontalrow having an assigned binary pixel value of 0. In another example, theintegrated circuit device is programmed to determine the first pixelfrom the right side of the horizontal row having an assigned binarypixel value of 0 and determine the last pixel from the right side ofhorizontal row having an assigned binary pixel value of 0.

Where a binary pixel value is assigned to each pixel in the greyscaleimage along a vertical column, in some the integrated circuit device isprogrammed to determine the first pixel along the vertical column havinga binary pixel value of 1 and determine the last pixel along thevertical column having a binary pixel value of 1. In one example, theintegrated circuit device is programmed to determine the first pixelfrom the top of the vertical column having an assigned binary pixelvalue of 1 and determine the last pixel from the top of the verticalcolumn having an assigned binary pixel value of 1. In another example,the integrated circuit device is programmed to determine the first pixelfrom the bottom of the vertical column having an assigned binary pixelvalue of 1 and determine the last pixel from the bottom of the verticalcolumn having an assigned binary pixel value of 1. In other embodiments,the integrated circuit device is programmed to determine the first pixelalong a vertical column having a binary pixel value of 0 and determinethe last pixel in the vertical column having a binary pixel value of 0.In one example, the integrated circuit device is programmed to determinethe first pixel from the top of the vertical column having an assignedbinary pixel value of 0 and determine the last pixel from the top of thevertical column having an assigned binary pixel value of 0. In anotherexample, the integrated circuit device is programmed to determine thefirst pixel from the bottom of the vertical column having an assignedbinary pixel value of 0 and determine the last pixel from bottom of thevertical column having an assigned binary pixel value of 0.

In some embodiments, the integrated circuit device is programmed todetermine one or more properties of the object based on the calculatedimage moment and generated image. For example, the integrated circuitdevice is programmed to determine the size of the cell, the center ofmass of the cell or the eccentricity of the cell based on the calculatedimage moment and generated image of the object (e.g., image or imagemask). In these embodiments, the integrated circuit device is programmedto calculate one or more image moments and then determine thecharacteristic of the cell using both the calculated image moment andthe image of the object.

In some instances, the center of mass may be calculated from the imagemoment and the generated image of the object. For example, theintegrated circuit device may be programmed to determine the center ofmass of the object from the calculated image moment and the generatedimage according to:

${{Center}\mspace{14mu}{of}\mspace{14mu}{Mass}} = \frac{M_{1,0}}{M_{0,0}}$

In other instances, the orientation of the object may be calculated fromthe image moment and the generated image of the object. For example, theintegrated circuit device is programmed to determine the orientation ofthe object from the calculated image moment and the generated imageaccording to:

${{Orientation}\mspace{14mu}{of}\mspace{14mu}{cell}} = {\frac{1}{2}\arctan\frac{2\left( {M_{1,1} - \frac{M_{1,0}M_{0,1}}{M_{0,0}}} \right)}{M_{2,0} - \frac{M_{2,0}^{2}}{M_{0,0}} - M_{0,2} + \frac{M_{0,2}^{2}}{M_{0,0}}}}$

In still other instances, the eccentricity of the object may becalculated from the image moment and the generated image of the object.For example, the integrated circuit device is programmed to determinethe eccentricity of the object from the calculated image moment and thegenerated image according to:

${Eccentricity} = \frac{\begin{matrix}{M_{2,0} - \frac{M_{2,0}^{2}}{M_{0,0}^{2}} + M_{0,2} - \frac{M_{0,2}^{2}}{M_{0,0}^{2}} -} \\\sqrt{{4\left( {M_{1,1} - \frac{M_{1,0}M_{0,1}}{M_{0,0}^{2}}} \right)^{2}} + \left( {M_{2,0} - \frac{M_{2,0}^{2}}{M_{0,0}} - M_{0,2} + \frac{M_{0,2}^{2}}{M_{0,0}}} \right)^{2}}\end{matrix}}{\begin{matrix}{M_{2,0} - \frac{M_{2,0}^{2}}{M_{0,0}^{2}} + M_{0,2} - \frac{M_{0,2}^{2}}{M_{0,0}^{2}} +} \\\sqrt{{4\left( {M_{1,1} - \frac{M_{1,0}M_{0,1}}{M_{0,0}^{2}}} \right)^{2}} + \left( {M_{2,0} - \frac{M_{2,0}^{2}}{M_{0,0}} - M_{0,2} + \frac{M_{0,2}^{2}}{M_{0,0}}} \right)^{2}}\end{matrix}}$

In certain embodiments, the integrated circuit device is programmed toassess one or more of: 1) the calculated size of the object; 2) thecenter of mass of the object; and 3) the eccentricity of the object andidentify whether the object is an aggregate of particles (e.g., a cellaggregate) or a single particle (e.g., a single cell). In someinstances, the integrated circuit device is programmed to determine thatthe object is an aggregate based on the calculated size of the object.In other instances, the integrated circuit device is programmed todetermine that the object is an aggregate based on the calculated centerof mass of the object. In yet other instances, the integrated circuitdevice is programmed to determine that the object is an aggregate basedon the calculated eccentricity of the object.

In certain instances, the integrated circuit device is programmed to: 1)assess one or more properties of the object based on the calculatedimage moment and spatial data; and 2) assess light scatter detectoroutput signals from the object in the interrogation region of the flowstream. In some embodiments, the integrated circuit device is programmedto assess output signals from a forward scatter light detector. In otherembodiments, the integrated circuit device is programmed to assessoutput signals from a side scatter light detector. In certainembodiments, the integrated circuit device is programmed to assess thelight scatter detector output signals for one or more of the pulsewidth, the pulse height and pulse area.

In certain instances, the integrated circuit device is programmed to: 1)assess one or more properties of the object based on the calculatedimage moment and generated image; and 2) assess a light scatter detectoroutput signal from the object in the interrogation region of the flowstream. In these embodiments, light scatter detector output signal maybe a forward light scatter detector output or a side light scatterdetector output or a combination thereof. In embodiments, the lightscatter detector output signal may be a signal pulse width, a signalpulse height and a signal pulse area or a combination thereof. In oneexample, the integrated circuit device is programmed to assess one ormore properties of the object (size, center of mass, eccentricity) basedon the calculated image moment and generated image and assess the pulsewidth of a light scatter detector output signal. In another example, theintegrated circuit device is programmed to assess one or more propertiesof the object (size, center of mass, eccentricity) based on thecalculated image moment and generated image and assess the pulse heightof a light scatter detector output signal. In yet another example, theintegrated circuit device is programmed to assess one or more propertiesof the object (size, center of mass, eccentricity) based on thecalculated image moment and generated image and assess the pulse area ofa light scatter detector output signal.

In certain embodiments, the integrated circuit device is programmedto: 1) assess each of the pulse width, the pulse height and pulse areaof a light scatter detector output signal, followed by 2) assess one ormore properties of the object (size, center of mass, eccentricity) basedon the calculated image moment and spatial data. In these embodiments,the light scatter detector output signals may be collected from one ormore of a side scatter detector and a forward scatter detector. In someinstances, light scatter detector output signals used according to theseembodiments are collected from a side scatter detector. In otherinstances, light scatter detector output signals used are collected froma forward scatter detector. In yet other instances, light scatterdetector output signals used are collected from both a side scatterdetector and a forward scatter detector.

In certain embodiments, the integrated circuit device is programmedto: 1) assess each of the pulse width, the pulse height and pulse areaof a light scatter detector output signal, followed by 2) assess one ormore properties of the object (size, center of mass, eccentricity) basedon the calculated image moment and generated image. In theseembodiments, the light scatter detector output signals may be collectedfrom one or more of a side scatter detector and a forward scatterdetector. In some instances, light scatter detector output signals usedaccording to these embodiments are collected from a side scatterdetector. In other instances, light scatter detector output signals usedare collected from a forward scatter detector. In yet other instances,light scatter detector output signals used are collected from both aside scatter detector and a forward scatter detector.

In some instances, the integrated circuit device is programmed todetermine that the object is a horizontal cell aggregate where two ormore cells are aligned together across a horizontal axis of the flowstream, such as 3 or more cells, such as 4 or more cells and including 5or more cells. In other instances, the integrated circuit device isprogrammed to determine that the object is a vertical cell aggregatewhere two or more cells are aligned together along a vertical axis(i.e., longitudinal axis) of the flow stream, such as 3 or more cells,such as 4 or more cells and including 5 or more cells. In yet otherinstances, the integrated circuit device is programmed to determine thatthe object is combination cell aggregate having two or more cellsaligned together across a horizontal axis and having two or more cellsaligned together along a vertical axis.

In some embodiments, the integrated circuit device is programmed tocalculate the spatial data from the frequency-encoded data of the objectin the flow stream. In these embodiments, the integrated circuit deviceis programmed to calculate the spatial data by performing a transform offrequency-encoded data. In one example, the spatial data is calculatedby performing a Fourier transform (FT) of the frequency-encoded data. Inanother example, the spatial data is calculated by performing a discreteFourier transform (DFT) of the frequency-encoded data. In yet anotherexample, the spatial data is calculated by performing a short timeFourier transform (STFT) of the frequency-encoded data. In still anotherexample, the spatial data is calculated with a digital lock-in amplifierto heterodyne and de-multiplex the frequency-encoded data.

In certain embodiments, the integrated circuit device is programmed tomake a sorting decision (as described above) based on the generatedimage or based on a calculated parameter (e.g., center of mass,eccentricity, etc.). In these embodiments, analysis includes classifyingand counting particles such that each particle is present as a set ofdigitized parameter values. The subject integrated circuit device may beprogrammed to trigger a sorting component based on a selected parameterin order to distinguish the particles of interest from background andnoise.

Kits

Aspects of the present disclosure further include kits, where kitsinclude one or more of the integrated circuit devices described herein.In some embodiments, kits may further include programming for thesubject systems, such as in the form of a computer readable medium(e.g., flash drive, USB storage, compact disk, DVD, Blu-ray disk, etc.)or instructions for downloading the programming from an internet webprotocol or cloud server. Kits may further include instructions forpracticing the subject methods. These instructions may be present in thesubject kits in a variety of forms, one or more of which may be presentin the kit. One form in which these instructions may be present is asprinted information on a suitable medium or substrate, e.g., a piece orpieces of paper on which the information is printed, in the packaging ofthe kit, in a package insert, and the like. Yet another form of theseinstructions is a computer readable medium, e.g.; diskette, compact disk(CD), portable flash drive, and the like, on which the information hasbeen recorded. Yet another form of these instructions that may bepresent is a website address which may be used via the internet toaccess the information at a removed site.

Utility

The subject systems, methods and computer systems find use in a varietyof applications where it is desirable to analyze and sort particlecomponents in a sample in a fluid medium, such as a biological sample.In some embodiments, the systems and methods described herein find usein flow cytometry characterization of biological samples labelled withfluorescent tags. In other embodiments, the systems and methods find usein spectroscopy of emitted light. In addition, the subject systems andmethods find use in increasing the obtainable signal from lightcollected from a sample (e.g., in a flow stream). In certain instances,the present disclosure finds use in enhancing measurement of lightcollected from a sample that is irradiated in a flow stream in a flowcytometer. Embodiments of the present disclosure find use where it isdesirable to provide a flow cytometer with improved cell sortingaccuracy, enhanced particle collection, particle charging efficiency,more accurate particle charging and enhanced particle deflection duringcell sorting.

Embodiments of the present disclosure also find use in applicationswhere cells prepared from a biological sample may be desired forresearch, laboratory testing or for use in therapy. In some embodiments,the subject methods and devices may facilitate obtaining individualcells prepared from a target fluidic or tissue biological sample. Forexample, the subject methods and systems facilitate obtaining cells fromfluidic or tissue samples to be used as a research or diagnosticspecimen for diseases such as cancer. Likewise, the subject methods andsystems may facilitate obtaining cells from fluidic or tissue samples tobe used in therapy. Methods and devices of the present disclosure allowfor separating and collecting cells from a biological sample (e.g.,organ, tissue, tissue fragment, fluid) with enhanced efficiency and lowcost as compared to traditional flow cytometry systems.

Although the foregoing invention has been described in some detail byway of illustration and example for purposes of clarity ofunderstanding, it is readily apparent to those of ordinary skill in theart in light of the teachings of this invention that certain changes andmodifications may be made thereto without departing from the spirit orscope of the appended claims.

Accordingly, the preceding merely illustrates the principles of theinvention. It will be appreciated that those skilled in the art will beable to devise various arrangements which, although not explicitlydescribed or shown herein, embody the principles of the invention andare included within its spirit and scope. Furthermore, all examples andconditional language recited herein are principally intended to aid thereader in understanding the principles of the invention and the conceptscontributed by the inventors to furthering the art, and are to beconstrued as being without limitation to such specifically recitedexamples and conditions. Moreover, all statements herein recitingprinciples, aspects, and embodiments of the invention as well asspecific examples thereof, are intended to encompass both structural andfunctional equivalents thereof. Additionally, it is intended that suchequivalents include both currently known equivalents and equivalentsdeveloped in the future, i.e., any elements developed that perform thesame function, regardless of structure. Moreover, nothing disclosedherein is intended to be dedicated to the public regardless of whethersuch disclosure is explicitly recited in the claims.

The scope of the present invention, therefore, is not intended to belimited to the exemplary embodiments shown and described herein. Rather,the scope and spirit of present invention is embodied by the appendedclaims. In the claims, 35 U.S.C. § 112(f) or 35 U.S.C. § 112(6) isexpressly defined as being invoked for a limitation in the claim onlywhen the exact phrase “means for” or the exact phrase “step for” isrecited at the beginning of such limitation in the claim; if such exactphrase is not used in a limitation in the claim, then 35 U.S.C. § 112(f) or 35 U.S.C. § 112(6) is not invoked.

1.-36. (canceled)
 37. A system comprising: a light source configured toirradiate a sample comprising cells in a flow stream; a light detectionsystem comprising a photodetector; and a processor comprising memoryoperably coupled to the processor wherein the memory comprisesinstructions stored thereon, which when executed by the processor, causethe processor to: generate spatial data of an object in the flow streamin an interrogation region to produce spatial data of the object; anddetermine whether the object in the flow stream is a cell aggregatebased on the spatial data.
 38. The system according to claim 37, whereinthe light detection system comprises a photodetector configured todetect light scatter from the sample in the flow stream. 39-40.(canceled)
 41. The system according to claim 37, wherein the memorycomprises instructions stored thereon, which when executed by theprocessor, cause the processor to determine the size of the object basedon the spatial data.
 42. The system according to claim 37, wherein thememory comprises instructions stored thereon, which when executed by theprocessor, cause the processor to determine the center of mass of theobject based on the spatial data.
 43. The system according to claim 37,wherein the memory comprises instructions stored thereon, which whenexecuted by the processor, cause the processor to determine theeccentricity of the object based on the spatial data.
 44. The systemaccording to claim 37, wherein the memory comprises instructions storedthereon, which when executed by the processor, cause the processor tocalculate an image moment of the object based on the spatial data.45-48. (canceled)
 49. The system according to claim 37, wherein thememory comprises instructions stored thereon, which when executed by theprocessor, cause the processor to generate an image of the object fromthe spatial data.
 50. The system according to claim 49, wherein thememory comprises instructions stored thereon, which when executed by theprocessor, cause the processor to generate an image mask of the object.51. The system according to claim 50, wherein the memory comprisesinstructions stored thereon, which when executed by the processor, causethe processor to: generate a greyscale image of the object in the flowstream; determine a pixel intensity threshold value from the greyscaleimage; compare each pixel from the greyscale image against thedetermined threshold value; and convert each pixel to a binary pixelvalue.
 52. The system according to claim 51, wherein the image maskcomprises pixels having a pixel value of
 1. 53-55. (canceled)
 56. Thesystem according to claim 37, wherein the memory comprises instructionsstored thereon, which when executed by the processor, cause theprocessor to determine that the object is a single cell.
 57. The systemaccording to claim 37, wherein the memory comprises instructions storedthereon, which when executed by the processor, cause the processor todetermine that the object is a cell aggregate comprising two or morecells.
 58. The system according to claim 57, wherein the memorycomprises instructions stored thereon, which when executed by theprocessor, cause the processor to determine that the cell aggregatecomprises two or more cells that are aligned together along thelongitudinal axis of the flow stream.
 59. The system according to claim57, wherein the memory comprises instructions stored thereon, which whenexecuted by the processor, cause the processor to determine that thecell aggregate comprises two or more cells that are aligned togetheralong the horizontal axis of the flow stream.
 60. The system accordingto claim 37, comprising an integrated circuit component programmed forgenerating one or more of: spatial data of the object in the flowstream; an image of the object in the flow stream; or an image mask ofthe object. 61-63. (canceled)
 64. The system according to claim 37,wherein the light source comprises a light beam generator componentconfigured to generate at least a first beam of frequency shifted lightand a second beam of frequency shifted light. 65-75. (canceled)
 76. Thesystem according to claim 37, wherein the light source comprises alaser.
 77. (canceled)
 78. The system according to claim 37, wherein thesystem is a flow cytometer.
 79. The system according to claim 37,further comprising a cell sorter.
 80. (canceled)
 81. The systemaccording to claim 37, wherein the system further comprises: a flow cellnozzle comprising an orifice; and a sample interrogation region in fluidcommunication with the flow cell nozzle orifice for irradiating thesample in the flow stream with the light source.
 82. (canceled) 83-112.(canceled)